Release Notes

Version History

This table tracks the meta-package versions and the version of each Qiskit element installed:

Table 16 Version History

Qiskit Metapackage Version	qiskit-terra	qiskit-aer	qiskit-ignis	qiskit-ibmq-provider	qiskit-aqua	Release Date
0.43.1	0.24.1	0.12.0		0.20.2		2023-06-02
0.43.0	0.24.0	0.12.0		0.20.2		2023-05-04
0.42.1	0.23.3	0.12.0		0.20.2		2023-03-21
0.42.0	0.23.2	0.12.0		0.20.2		2023-03-10
0.41.1	0.23.2	0.11.2		0.20.1		2023-02-23
0.41.0	0.23.1	0.11.2		0.20.0		2023-01-31
0.40.0	0.23.0	0.11.2		0.19.2		2023-01-26
0.39.5	0.22.4	0.11.2		0.19.2		2023-01-17
0.39.4	0.22.3	0.11.2		0.19.2		2022-12-08
0.39.3	0.22.3	0.11.1		0.19.2		2022-11-25
0.39.2	0.22.2	0.11.1		0.19.2		2022-11-03
0.39.1	0.22.1	0.11.1		0.19.2		2022-11-02
0.39.0	0.22.0	0.11.0		0.19.2		2022-10-13
0.38.0	0.21.2	0.11.0		0.19.2		2022-09-14
0.37.2	0.21.2	0.10.4		0.19.2		2022-08-23
0.37.1	0.21.1	0.10.4		0.19.2		2022-07-28
0.37.0	0.21.0	0.10.4		0.19.2		2022-06-30
0.36.2	0.20.2	0.10.4	0.7.1	0.19.1		2022-05-18
0.36.1	0.20.1	0.10.4	0.7.0	0.19.1		2022-04-21
0.36.0	0.20.0	0.10.4	0.7.0	0.19.0		2022-04-06
0.35.0	0.20.0	0.10.3	0.7.0	0.18.3		2022-03-31
0.34.2	0.19.2	0.10.3	0.7.0	0.18.3		2022-02-09
0.34.1	0.19.1	0.10.2	0.7.0	0.18.3		2022-01-05
0.34.0	0.19.1	0.10.1	0.7.0	0.18.3		2021-12-20
0.33.1	0.19.1	0.9.1	0.7.0	0.18.2		2021-12-10
0.33.0	0.19.0	0.9.1	0.7.0	0.18.1		2021-12-06
0.32.1	0.18.3	0.9.1	0.6.0	0.18.1	0.9.5	2021-11-22
0.32.0	0.18.3	0.9.1	0.6.0	0.18.0	0.9.5	2021-11-10
0.31.0	0.18.3	0.9.1	0.6.0	0.17.0	0.9.5	2021-10-12
0.30.1	0.18.3	0.9.0	0.6.0	0.16.0	0.9.5	2021-09-29
0.30.0	0.18.2	0.9.0	0.6.0	0.16.0	0.9.5	2021-09-16
0.29.1	0.18.2	0.8.2	0.6.0	0.16.0	0.9.5	2021-09-10
0.29.0	0.18.1	0.8.2	0.6.0	0.16.0	0.9.4	2021-08-02
0.28.0	0.18.0	0.8.2	0.6.0	0.15.0	0.9.4	2021-07-13
0.27.0	0.17.4	0.8.2	0.6.0	0.14.0	0.9.2	2021-06-15
0.26.2	0.17.4	0.8.2	0.6.0	0.13.1	0.9.1	2021-05-19
0.26.1	0.17.4	0.8.2	0.6.0	0.13.1	0.9.1	2021-05-18
0.26.0	0.17.3	0.8.2	0.6.0	0.13.1	0.9.1	2021-05-11
0.25.4	0.17.2	0.8.2	0.6.0	0.12.3	0.9.1	2021-05-05
0.25.3	0.17.1	0.8.2	0.6.0	0.12.3	0.9.1	2021-04-29
0.25.2	0.17.1	0.8.1	0.6.0	0.12.3	0.9.1	2021-04-21
0.25.1	0.17.1	0.8.1	0.6.0	0.12.2	0.9.1	2021-04-15
0.25.0	0.17.0	0.8.0	0.6.0	0.12.2	0.9.0	2021-04-02
0.24.1	0.16.4	0.7.6	0.5.2	0.12.2	0.8.2	2021-03-24
0.24.0	0.16.4	0.7.6	0.5.2	0.12.1	0.8.2	2021-03-04
0.23.6	0.16.4	0.7.5	0.5.2	0.11.1	0.8.2	2021-02-18
0.23.5	0.16.4	0.7.4	0.5.2	0.11.1	0.8.2	2021-02-08
0.23.4	0.16.3	0.7.3	0.5.1	0.11.1	0.8.1	2021-01-28
0.23.3	0.16.2	0.7.3	0.5.1	0.11.1	0.8.1	2021-01-26
0.23.2	0.16.1	0.7.2	0.5.1	0.11.1	0.8.1	2020-12-15
0.23.1	0.16.1	0.7.1	0.5.1	0.11.1	0.8.1	2020-11-12
0.23.0	0.16.0	0.7.0	0.5.0	0.11.0	0.8.0	2020-10-16
0.22.0	0.15.2	0.6.1	0.4.0	0.10.0	0.7.5	2020-10-05
0.21.0	0.15.2	0.6.1	0.4.0	0.9.0	0.7.5	2020-09-16
0.20.1	0.15.2	0.6.1	0.4.0	0.8.0	0.7.5	2020-09-08
0.20.0	0.15.1	0.6.1	0.4.0	0.8.0	0.7.5	2020-08-10
0.19.6	0.14.2	0.5.2	0.3.3	0.7.2	0.7.3	2020-06-25
0.19.5	0.14.2	0.5.2	0.3.2	0.7.2	0.7.3	2020-06-19
0.19.4	0.14.2	0.5.2	0.3.0	0.7.2	0.7.2	2020-06-16
0.19.3	0.14.1	0.5.2	0.3.0	0.7.2	0.7.1	2020-06-02
0.19.2	0.14.1	0.5.1	0.3.0	0.7.1	0.7.1	2020-05-14
0.19.1	0.14.1	0.5.1	0.3.0	0.7.0	0.7.0	2020-05-01
0.19.0	0.14.0	0.5.1	0.3.0	0.7.0	0.7.0	2020-04-30
0.18.3	0.13.0	0.5.1	0.3.0	0.6.1	0.6.6	2020-04-24
0.18.2	0.13.0	0.5.0	0.3.0	0.6.1	0.6.6	2020-04-23
0.18.1	0.13.0	0.5.0	0.3.0	0.6.0	0.6.6	2020-04-20
0.18.0	0.13.0	0.5.0	0.3.0	0.6.0	0.6.5	2020-04-09
0.17.0	0.12.0	0.4.1	0.2.0	0.6.0	0.6.5	2020-04-01
0.16.2	0.12.0	0.4.1	0.2.0	0.5.0	0.6.5	2020-03-20
0.16.1	0.12.0	0.4.1	0.2.0	0.5.0	0.6.4	2020-03-05
0.16.0	0.12.0	0.4.0	0.2.0	0.5.0	0.6.4	2020-02-27
0.15.0	0.12.0	0.4.0	0.2.0	0.4.6	0.6.4	2020-02-06
0.14.1	0.11.1	0.3.4	0.2.0	0.4.5	0.6.2	2020-01-07
0.14.0	0.11.0	0.3.4	0.2.0	0.4.4	0.6.1	2019-12-10
0.13.0	0.10.0	0.3.2	0.2.0	0.3.3	0.6.1	2019-10-17
0.12.2	0.9.1	0.3.0	0.2.0	0.3.3	0.6.0	2019-10-11
0.12.1	0.9.0	0.3.0	0.2.0	0.3.3	0.6.0	2019-09-30
0.12.0	0.9.0	0.3.0	0.2.0	0.3.2	0.6.0	2019-08-22
0.11.2	0.8.2	0.2.3	0.1.1	0.3.2	0.5.5	2019-08-20
0.11.1	0.8.2	0.2.3	0.1.1	0.3.1	0.5.3	2019-07-24
0.11.0	0.8.2	0.2.3	0.1.1	0.3.0	0.5.2	2019-07-15
0.10.5	0.8.2	0.2.1	0.1.1	0.2.2	0.5.2	2019-06-27
0.10.4	0.8.2	0.2.1	0.1.1	0.2.2	0.5.1	2019-06-17
0.10.3	0.8.1	0.2.1	0.1.1	0.2.2	0.5.1	2019-05-29
0.10.2	0.8.0	0.2.1	0.1.1	0.2.2	0.5.1	2019-05-24
0.10.1	0.8.0	0.2.0	0.1.1	0.2.2	0.5.0	2019-05-07
0.10.0	0.8.0	0.2.0	0.1.1	0.2.1	0.5.0	2019-05-06
0.9.0	0.8.0	0.2.0	0.1.1	0.1.1	0.5.0	2019-05-02
0.8.1	0.7.2	0.1.1	0.1.0			2019-05-01
0.8.0	0.7.1	0.1.1	0.1.0			2019-03-05
0.7.3	>=0.7,<0.8	>=0.1,<0.2				2019-02-19
0.7.2	>=0.7,<0.8	>=0.1,<0.2				2019-01-22
0.7.1	>=0.7,<0.8	>=0.1,<0.2				2019-01-17
0.7.0	>=0.7,<0.8	>=0.1,<0.2				2018-12-14

Note

For the 0.7.0, 0.7.1, and 0.7.2 meta-package releases the Gestion des versions de Qiskit policy was not formalized yet.

Notable Changes

Qiskit 0.43.1

Terra 0.24.1

Prelude

Qiskit Terra 0.24.1 is the first patch release to 0.24.0. This fixes some bugs that have been discovered since the release of 0.24.0.

Upgrade Notes

• Changed QuantumCircuit.assign_parameters() to bind assigned integer and float values directly into the parameters of Instruction instances in the circuit rather than binding the values wrapped within a ParameterExpression. This change should have little user impact as float(QuantumCircuit.data[i].operation.params[j]) still produces a float (and is the only way to access the value of a ParameterExpression). Also, Instruction() parameters could already be float as well as a ParameterExpression, so code dealing with instruction parameters should already handle both cases. The most likely chance for user impact is in code that uses isinstance to check for ParameterExpression and behaves differently depending on the result. Additionally, qpy serializes the numeric value in a bound ParameterExpression at a different precision than a float (see also the related bug fix note about QuantumCircuit.assign_parameters()).

Bug Fixes

• Updated plot_gate_map(), plot_error_map(), and plot_circuit_layout() to support 433 qubit heavy-hex coupling maps. This allows coupling map visualizations for IBM Quantum’s ibm_seattle backend.

• Changed the binding of numeric values with QuantumCircuit.assign_parameters() to avoid a mismatch between the values of circuit instruction parameters and corresponding parameter keys in the circuit’s calibration dictionary. Fixed #9764 and #10166. See also the related upgrade note regarding QuantumCircuit.assign_parameters().

• Fixed a bug in BlockCollapser where classical bits were ignored when collapsing a block of nodes.

• Fixed a bug in replace_block_with_op() and replace_block_with_op() that led to ignoring classical bits.

• Fixed a bug in QuantumCircuit.compose() where the SwitchCaseOp.target attribute in the subcircuit was not correctly mapped to a register in the base circuit.

• Fix a bug in RZXCalibrationBuilder where calling calibration with wrong parameters would crash instead of raising an exception.

• Fixed an issue with the BooleanExpression.from_dimacs_file() constructor method where the exception type raised when tweedledum wasn’t installed was not the expected MissingOptionalLibrary. Fixed #10079

• Using initial_layout in calls to transpile() will no longer error if the circuit contains qubits not in any registers, or qubits that exist in more than one register. See #10125.

• Fixed the gate decomposition of multi-controlled Z rotation gates added via QuantumCircuit.mcrz(). Previously, this method implemented a multi-controlled phase gate, which has a relative phase difference to the Z rotation. To obtain the previous QuantumCircuit.mcrz() behaviour, use QuantumCircuit.mcp().

• Fixed an issue with the PassManagerConfig.from_backend() constructor when building a PassManagerConfig object from a BackendV1 instance that didn’t have a coupling map attribute defined. Previously, the constructor would incorrectly create a CouplingMap object with 0 qubits instead of using None. Fixed #10171

• Fixes a bug introduced in Qiskit 0.24.0 where numeric rotation angles were no longer substituted for symbolic ones before preparing for two-qubit synthesis. This caused an exception to be raised because the synthesis routines require numberic matrices.

• Fix a bug in which running Optimize1qGatesDecomposition in parallel would raise an error due to OneQubitGateErrorMap not being picklable.

• Fix a bug in the VF2Layout and VF2PostLayout passes where the passes were failing to account for the 1 qubit error component when evaluating a potential layout.

Aer 0.12.0

No change

IBM Q Provider 0.20.2

No change

Qiskit 0.43.0

Terra 0.24.0

Prelude

This is a major feature release that includes numerous new features and bugfixes.

This release is the final release with support for running Qiskit with Python 3.7. Starting in the next minor version release Python >=3.8 will be required to run Qiskit.

The highlights of this release:

QuantumInstance, OpFlow, and algorithms usage deprecation

This release officially deprecates the QuantumInstance class (and its associated helper methods and classes), the qiskit.opflow module, and any usage of those in qiskit.algorithms. This deprecation comes from a long thread of work that started in Qiskit Terra 0.21.0 to refactor the qiskit.algorithms module to be based on the computational primitives. There are associated migration guides for any existing users to migrate to the new workflow:

• QuantumInstance migration guide: https://qisk.it/qi_migration

• Opflow migration guide: https://qisk.it/opflow_migration

• Algorithms migration guide: https://qisk.it/algo_migration

OpenQASM2 improvements

This release includes a major refactoring for the OpenQASM 2.0 support in Qiskit. The first change is the introduction of a new parser for OpenQASM 2.0 in the qiskit.qasm2 module. This new module replaces the existing qiskit.qasm module. The new parser is more explicit and correct with respect to the language specification. It is also implemented in Rust and is significantly faster than the previous parser. Paired with the new parser the OpenQASM 2.0 exporter underwent a large refactor that improved the correctness of the output when using the QuantumCircuit.qasm() method to generate QASM output from a QuantumCircuit object.

Transpiler support for devices with disjoint connectivity

The transpiler now supports targeting backends with disjoint connectivity. Previously, the transpiler only supported backends which were fully connected (where there is a path to run operations between all pairs of qubits in the backend). Now, if a backend has disconnected connectivity the transpiler is able to reason about how to apply layout (Layout Stage) and routing (Routing Stage) for the backend. If the input circuit is not able to be executed on the hardware given the lack of connectivity between connected components, a descriptive error will be returned.

For example, the Heron device outlined in IBM Quantum’s hardware roadmap describes a future backend which will have shared control hardware and real-time classical communication between separate quantum processors. This support enables the Target to accurately model these types of future devices or other hardware with similar constraints.

Switch Operation

This release adds a new control flow operation, the switch statement. This is implemented using a new operation class SwitchCaseOp and the QuantumCircuit.switch() method. This allows switching on a numeric input (such as a classical register or bit) and executing the circuit that corresponds to the matching value.

New Features

• Added the functions add_deprecation_to_docstring(), deprecate_arg(), and deprecate_func() to the qiskit.utils module.

  add_deprecation_to_docstring() will rewrite the function’s docstring to include a Sphinx .. deprecated:: directive so that the deprecation shows up in docs and with help(). The deprecation decorators from qiskit.utils call add_deprecation_to_docstring() already for you; but you can call it directly if you are using different mechanisms for deprecations.

  @deprecate_func replaces @deprecate_function and is used to deprecate an entire function. It will auto-generate most of the deprecation message for you.

  @deprecate_arg replaces @deprecate_arguments and is used to deprecate an argument on a function. It will generate a more useful message than the previous function. It is also more flexible, for example it allows setting a predicate so that you only deprecate certain situations, such as using a deprecated value or data type.

Transpiler Features

• Added an alternative way to specify in HLSConfig the list of synthesis methods used for a given high-level object. As before, a synthesis method can be specified as a tuple consisting of the name of the method and additional arguments. Additionally, a synthesis method can be specified as a tuple consisting of an instance of HighLevelSynthesisPlugin and additional arguments. Moreover, when there are no additional arguments, a synthesis method can be specified simply by name or by an instance of HighLevelSynthesisPlugin. The following example illustrates the new functionality:

  from qiskit import QuantumCircuit
  from qiskit.circuit.library.generalized_gates import PermutationGate
  from qiskit.transpiler import PassManager
  from qiskit.transpiler.passes.synthesis.high_level_synthesis import HLSConfig, HighLevelSynthesis
  from qiskit.transpiler.passes.synthesis.high_level_synthesis import ACGSynthesisPermutation
  
  qc = QuantumCircuit(6)
  qc.append(PermutationGate([1, 2, 3, 0]), [1, 2, 3, 4])
  
  # All of the ways to specify hls_config are equivalent
  hls_config = HLSConfig(permutation=[("acg", {})])
  hls_config = HLSConfig(permutation=["acg"])
  hls_config = HLSConfig(permutation=[(ACGSynthesisPermutation(), {})])
  hls_config = HLSConfig(permutation=[ACGSynthesisPermutation()])
  
  # The hls_config can then be passed as an argument to HighLevelSynthesis
  pm = PassManager(HighLevelSynthesis(hls_config=hls_config))
  qc_synthesized = pm.run(qc)
  

• Added support to the CouplingMap object to have a disjoint connectivity. Previously, a CouplingMap could only be constructed if the graph was connected. This will enable using CouplingMap to represent hardware with disjoint qubits, such as hardware with qubits on multiple separate chips.

• Added a new method CouplingMap.connected_components() which is used to get a list of CouplingMap component subgraphs for a disjoint CouplingMap. If the CouplingMap object is connected this will just return a single CouplingMap equivalent to the original.

• Added new rules to the built-in EquivalenceLibrary instance: qiskit.circuit.equivalence_library.SessionEquivalenceLibrary. The new rules added are:

  • CXGate into ECRGate and 1-qubit Clifford gates (up to a global phase).

  • HGate into SXGate and SGate (up to a global phase).

  • HGate into SXdgGate and SdgGate (up to a global phase).

• Added high-level-synthesis plugins for LinearFunction and for qiskit.quantum_info.Clifford, extending the set of synthesis methods that can be called from HighLevelSynthesis transpiler pass.

  For LinearFunction the available plugins are listed below:

  Plugin name	High-level synthesis plugin
  default	DefaultSynthesisLinearFunction
  kms	KMSSynthesisLinearFunction
  pmh	PMHSynthesisLinearFunction

  For qiskit.quantum_info.Clifford the available plugins are listed below:

  Plugin name	High-level synthesis plugin
  default	DefaultSynthesisClifford
  ag	AGSynthesisClifford
  bm	BMSynthesisClifford
  greedy	GreedySynthesisClifford
  layers	LayerSynthesisClifford
  lnn	LayerLnnSynthesisClifford

  Please refer to qiskit.synthesis documentation for more information about each individual method.

  The following example illustrates some of the new plugins:

  from qiskit.circuit import QuantumCircuit
  from qiskit.circuit.library import LinearFunction
  from qiskit.quantum_info import Clifford
  from qiskit.transpiler.passes.synthesis.high_level_synthesis import HLSConfig, HighLevelSynthesis
  
  # Create a quantum circuit with one linear function and one clifford
  qc1 = QuantumCircuit(3)
  qc1.cx(0, 1)
  qc1.swap(0, 2)
  lin_fun = LinearFunction(qc1)
  
  qc2 = QuantumCircuit(3)
  qc2.h(0)
  qc2.cx(0, 2)
  cliff = Clifford(qc2)
  
  qc = QuantumCircuit(4)
  qc.append(lin_fun, [0, 1, 2])
  qc.append(cliff, [1, 2, 3])
  
  # Choose synthesis methods that adhere to linear-nearest-neighbour connectivity
  hls_config = HLSConfig(linear_function=["kms"], clifford=["lnn"])
  
  # Synthesize
  qct = HighLevelSynthesis(hls_config)(qc)
  print(qct.decompose())
  

• Added a new transpiler pass, MinimumPoint which is used primarily as a pass to check a loop condition in a PassManager. This pass will track the state of fields in the property set over its past executions and set a boolean field when either a fixed point is reached over the backtracking depth or selecting the minimum value found if the backtracking depth is reached. This is an alternative to the FixedPoint which simply checks for a fixed value in a property set field between subsequent executions.

• Added a new method, swap_nodes(), to the DAGCircuit to allow swapping nodes which are partially connected. Partially connected here means that the two nodes share at least one edge (which represents a qubit or clbit). If the nodes do not share any edges a DAGCircuitError is raised.

• Add a new synthesis algorithm synth_cz_depth_line_mr() of a CZ circuit for linear nearest neighbor (LNN) connectivity in 2-qubit depth of 2n+2 using CX and phase gates (S, Sdg or Z). The synthesized circuit reverts the order of the qubits. The synthesis algorithm is based on the paper of Maslov and Roetteler (https://arxiv.org/abs/1705.09176).

• Add a new synthesis algorithm synth_clifford_depth_lnn() of a Clifford circuit for LNN connectivity in 2-qubit depth of 9n+4 (which is still not optimal), using the layered Clifford synthesis (synth_clifford_layers()), synth_cnot_depth_line_kms() to synthesize the CX layer in depth 5n, and synth_cz_depth_line_mr() to synthesize each of the CZ layers in depth 2n+2. This PR will be followed by another PR based on the recent paper of Maslov and Yang (https://arxiv.org/abs/2210.16195), that synthesizes the CX-CZ layers in depth 5n for LNN connectivity and performs further optimization, and hence reduces the depth of a Clifford circuit to 7n-4 for LNN connectivity.

• Equivalences between the controlled Pauli rotations and translations to two-Pauli rotations are now available in the equivalence library for Qiskit standard gates. This allows, for example, to translate a CRZGate to a RZZGate plus RZGate or a CRYGate to a single RZXGate plus single qubit gates:

  from qiskit.circuit import QuantumCircuit
  from qiskit.compiler import transpile
  
  angle = 0.123
  circuit = QuantumCircuit(2)
  circuit.cry(angle, 0, 1)
  
  basis = ["id", "sx", "x", "rz", "rzx"]
  transpiled = transpile(circuit, basis_gates=basis)
  print(transpiled.draw())
  

• Added a new option, copy_operations, to circuit_to_dag() to enable optionally disabling deep copying the operations from the input QuantumCircuit to the output QuantumCircuit. In cases where the input :class`~.QuantumCircuit` is not used anymore after conversion this deep copying is unnecessary overhead as any shared references wouldn’t have any potential unwanted side effects if the input QuantumCircuit is discarded.

• Added a new option, copy_operations, to dag_to_circuit() to enable optionally disabling deep copying the operations from the input DAGCircuit to the output QuantumCircuit. In cases where the input DAGCircuit is not used anymore after conversion this deep copying is unnecessary overhead as any shared references wouldn’t have any potential unwanted side effects if the input DAGCircuit is discarded.

• Added a new function passmanager_stage_plugins() to the qiskit.transpiler.preset_passmanagers.plugin module. This function is used to obtain a mapping from plugin names to their their class type. This enables identifying and querying any defined pass manager stage plugin’s documentation. For example:

  >>> from qiskit.transpiler.preset_passmanagers.plugin import passmanager_stage_plugins
  >>> passmanager_stage_plugins('routing')['lookahead'].__class__
  
  qiskit.transpiler.preset_passmanagers.builtin_plugins.LookaheadSwapPassManager
  
  >>> help(passmanager_stage_plugins('routing')['lookahead'])
  Help on BasicSwapPassManager in module qiskit.transpiler.preset_passmanagers.builtin_plugins object:
  
  class BasicSwapPassManager(qiskit.transpiler.preset_passmanagers.plugin.PassManagerStagePlugin)
  |  Plugin class for routing stage with :class:`~.BasicSwap`
  ...
  

• The transpiler pass Error now also accepts callable inputs for its msg parameter. If used these input callables will be passed the property_set attribute of the pass and are expected to return a string which will be used for the error message when the pass is run. For example:

  from qiskit.transpiler.passes import Error
  
  def error_message(property_set):
  
      size = property_set["size']
      return f"The circuit size is: {size}"
  
  error_pass = Error(error_message)
  

  When error_pass is included in a pass manager it will error using the message "The circuit size is: n" where n is the circuit size set in the property set (typically from the previous execution of the Size pass).

• The build_coupling_map() method has a new keyword argument, filter_idle_qubits which when set to True will remove any qubits from the output CouplingMap that don’t support any operations.

• The GateDirection transpiler pass can now correctly handle SwapGate instances that may be present in the circuit when executing on a circuit. In these cases if the swap gate’s qubit arguments are on the non-native direction of an edge, the pass will flip the argument order.

• The RZXCalibrationBuilder and RZXCalibrationBuilderNoEcho transpiler passes now will correctly use an ECRGate for the entangling gate if the backend’s native entangling gate is ECRGate. Previously, the passes would only function correctly if the entangling gate was CXGate.

• Added a new constructor for the Target class, Target.from_configuration(), which lets you construct a Target object from the separate object types for describing the constraints of a backend (e.g. basis gates, CouplingMap, BackendProperties, etc). For example:

  target = Target.from_configuration(
      basis_gates=["u", "cx", "measure"],
      coupling_map=CouplingMap.from_line(25),
  )
  

  This will construct a Target object that has UGate, CXGate, and Measure globally available on 25 qubits which are connected in a line.

• Added a new function synth_cnot_phase_aam() which is used to synthesize cnot phase circuits for all-to-all architectures using the Amy, Azimzadeh, and Mosca method. This function is identical to the available qiskit.transpiler.synthesis.graysynth() function but has a more descriptive name and is more logically placed in the package tree. This new function supersedes the legacy function which will likely be deprecated in a future release.

• Internal tweaks to the routing algorithm in SabreSwap, used in transpilation of non-dynamic circuits at all non-zero optimization levels, have sped up routing for very large circuits. For example, the time to route a depth-5 QuantumVolume circuit for a 1081-qubit heavy-hex coupling map is approximately halved.

• The runtime performance of the Optimize1qGatesDecomposition transpiler pass has been significantly improved. This was done by both rewriting all the computation for the pass in Rust and also decreasing the amount of intermediate objects created as part of the pass’s execution. This should also correspond to a similar improvement in the runtime performance of transpile() with the optimization_level keyword argument set to 1, 2, or 3.

• Add a new synthesis method synth_stabilizer_layers() of a stabilizer state into layers. It provides a similar decomposition to the synthesis described in Lemma 8 of Bravyi and Maslov, (arxiv:2003.09412) without the initial Hadamard-free sub-circuit which does not affect the stabilizer state.

• Add a new synthesis method synth_stabilizer_lnn() of a stabilizer state for linear nearest neighbor connectivity in 2-qubit depth of 2n+2 and two distinct CX layers, using CX and phase gates (S, Sdg or Z). The synthesis algorithm is based on the paper of Maslov and Roetteler (https://arxiv.org/abs/1705.09176).

• The SabreLayout pass now supports running against a target with a disjoint CouplingMap. When targeting a disjoint coupling the input DAGCircuit is split into its connected components of virtual qubits, each component is mapped to the connected components of the CouplingMap, layout is run on each connected component in isolation, and then all layouts are combined and returned. Note when the routing_pass argument is set the pass doesn’t support running with disjoint connectivity.

• The following layout and routing transpiler passes from the qiskit.transpiler.passes modules now will support accepting a Target object which is used to model the constraints of a target backend via the first positional argument (currently named either coupling_map or backend_properties).

  The list of passes with the new support for Target input are:

  • CSPLayout

  • FullAncillaAllocation

  • Layout2qDistance

  • NoiseAdaptiveLayout

  • SabreLayout

  • TrivialLayout

  • BasicSwap

  • BIPMapping

  • LayoutTransformation

  • LookaheadSwap

  • SabreSwap

  • StochasticSwap

  • CheckMap

• The pass manager construction helper function generate_embed_passmanager() will now also accept a Target for it’s sole positional argument (currently named coupling_map). This can be used to construct a layout embedding PassManager from a Target object instead of from a CouplingMap.

• The following layout and routing transpiler passes from the qiskit.transpiler.passes modules have a new keyword argument, target which takes in a Target object which is used to model the constraints of a target backend. If the target keyword argument is specified it will be used as the source of truth for any hardware constraints used in the operation of the transpiler pass. It will supersede any other arguments for specifying hardware constraints, typically those arguments which take a CouplingMap, InstructionScheduleMap or a basis gate list. The list of these passes with the new target argument are:

  • Unroller

  • PulseGate

  • RZXCalibrationBuilder

  • CommutativeCancellation

  • EchoRZXWeylDecomposition

  • Optimize1qGatesSimpleCommutation

  • Optimize1qGates

  • CheckCXDirection

  • ALAPSchedule

  • ASAPSchedule

  • ALAPScheduleAnalysis

  • ASAPScheduleAnalysis

  • DynamicalDecoupling

  • PadDynamicalDecoupling

  • TimeUnitConversion

• The pass manager construction helper function generate_scheduling() has a new keyword argument target which is used to specify a Target object to model the constraints of the target backend being compiled for when generating a new PassManager. If specified this new argument will supersede the other argument inst_map.

• The default plugin used by the UnitarySynthesis transpiler pass now chooses one and two-qubit unitary synthesis based on the error rates reported in the Target. In particular, it runs all possible synthesis methods supported by the plugin and chooses the option which will result in the lowest error. For a one-qubit decomposition, it can target Pauli basis (e.g. RZ-RX-RZ or RZ-RY-RZ), generic unitary basis (e.g. U), and a few others. For a two-qubit decomposition, it can target any supercontrolled basis (e.g. CNOT, iSWAP, B) or multiple controlled basis (e.g. CZ, CH, ZZ^.5, ZX^.2, etc.).

• The interface for UnitarySynthesisPlugin has two new optional properties supports_gate_lengths_by_qubit and supports_gate_errors_by_qubit which when set will add the fields gate_lengths_by_qubit and gate_errors_by_qubit respectively to the input options to the plugin’s run() method. These new fields are an alternative view of the data provided by gate_lengths and gate_errors but instead have the form: {(qubits,): [Gate, length]} (where Gate is the instance of Gate for that definition). This allows plugins to reason about working with gates of the same type but but that have different parameters set.

• Added a new transpiler pass, UnrollForLoops, which is used to unroll any ForLoopOp operations in a circuit. This pass unrolls for-loops when possible, if there are no ContinueLoopOp or BreakLoopOp inside the body block of the loop. For example:

  from qiskit.transpiler.passes import UnrollForLoops
  from qiskit import QuantumCircuit
  
  unroll_pass = UnrollForLoops()
  
  qc = QuantumCircuit(1)
  # For loop over range 5
  with qc.for_loop(range(5)) as i:
      qc.rx(i, 0)
  # Unroll loop into 5 rx gates
  unroll_pass(qc).draw("mpl")
  

  (Source code)

  

• Added a new parameter max_trials to pass VF2PostLayout which, when specified, limits the number of layouts discovered and compared when searching for the best layout. This differs from existing parameters call_limit and time_limit (which are used to limit the number of state visits performed by the VF2 algorithm and the total time spent by pass VF2PostLayout, respectively) in that it is used to place an upper bound on the time spent scoring potential layouts, which may be useful for larger devices.

• The CheckMap transpiler pass has a new keyword argument on its constructor, property_set_field. This argument can be used to specify a field in the property set to store the results of the analysis. Previously, it was only possible to store the result in the field "is_swap_mapped" (which is the default). This enables you to store the result of multiple instances of the pass in a PassManager in different fields.

Circuits Features

• Added a new gate class, GlobalPhaseGate, which can be used to add a global phase on the QuantumCircuit instance.

• Added a new attribute, layout, to the QuantumCircuit class. This attribute is typically populated by transpile() or PassManager.run() (when the Layout Stage and Routing Stage are run in the PassManager) and contains a TranspileLayout which contains the information about the permutation of the input circuit during transpile().

• Added a new argument, var_order, to the PhaseOracle class’s constructor to enable setting the order in which the variables in the logical expression are being considered. For example:

  from qiskit.tools.visualization import plot_histogram
  from qiskit.primitives import Sampler
  from qiskit.circuit.library import PhaseOracle
  from qiskit.algorithms import Grover, AmplificationProblem
  
  oracle = PhaseOracle('((A & C) | (B & D)) & ~(C & D)', var_order=['A', 'B', 'C', 'D'])
  problem = AmplificationProblem(oracle=oracle, is_good_state=oracle.evaluate_bitstring)
  grover = Grover(sampler=Sampler())
  result = grover.amplify(problem)
  print(result.circuit_results[0])
  

• A new OpenQASM 2 parser is available in qiskit.qasm2. This has two entry points: qasm2.load() and qasm2.loads(), for reading the source code from a file and from a string, respectively:

  import qiskit.qasm2
  program = """
    OPENQASM 2.0;
    include "qelib1.inc";
    qreg q[2];
    h q[0];
    cx q[0], q[1];
  """
  bell = qiskit.qasm2.loads(program)
  

  This new parser is approximately 10x faster than the existing ones at QuantumCircuit.from_qasm_file() and QuantumCircuit.from_qasm_str() for large files, and has less overhead on each call as well. The new parser is more extensible, customisable and generally also more type-safe; it will not attempt to output custom Qiskit objects when the definition in the OpenQASM 2 file clashes with the Qiskit object, unlike the current exporter. See the qiskit.qasm2 module documentation for full details and more examples.

• Improve the decomposition of multi-controlled Pauli-X and Pauli-Y rotations with QuantumCircuit.mcrx() and QuantumCircuit.mcry on :math:`n() controls to \(16n - 40\) CX gates, for \(n \geq 4\). This improvement is based on arXiv:2302.06377.

• Qiskit now supports the representation of switch statements, using the new SwitchCaseOp instruction and the QuantumCircuit.switch() method. This allows switching on a numeric input (such as a classical register or bit) and executing the circuit that corresponds to the matching value. Multiple values can point to the same circuit, and CASE_DEFAULT can be used as an always-matching label.

  You can also use a builder interface, similar to the other control-flow constructs to build up these switch statements:

  from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
  
  qreg = QuantumRegister(2)
  creg = ClassicalRegister(2)
  qc = QuantumCircuit(qreg, creg)
  
  qc.h([0, 1])
  qc.measure([0, 1], [0, 1])
  with qc.switch(creg) as case:
    with case(0):  # if the register is '00'
      qc.z(0)
    with case(1, 2):  # if the register is '01' or '10'
      qc.cx(0, 1)
    with case(case.DEFAULT):  # the default case
      qc.h(0)
  

  The switch statement has support throughout the Qiskit compiler stack; you can transpile() circuits containing it (if the backend advertises its support for the construct), and it will serialize to QPY.

  The switch statement is not currently a feature of OpenQASM 3, but it is under active design and consideration, which is expected to be adopted in the near future. Qiskit Terra has experimental support for exporting this statement to the OpenQASM 3 syntax proposed in the linked pull request, using an experimental feature flag. To export a switch statement circuit (such as the one created above) to OpenQASM 3 using this speculative support, do:

  from qiskit import qasm3
  
  qasm3.dumps(qc, experimental=qasm3.ExperimentalFeatures.SWITCH_CASE_V1)
  

Algorithms Features

• Added a new attribute eigenvalue_threshold to the AdaptVQE class for configuring a new kind of threshold to terminate the algorithm once the eigenvalue changes less than a set value.

• Added a new attribute gradient_threshold to the AdaptVQE class which will replace the threshold in the future. This new attribute behaves the same as the existing threshold attribute but has a more accurate name, given the introduction of additional threshold options in the class.

• Added the EstimationProblem.has_good_state attribute, which allows to check whether an EstimationProblem has a custom EstimationProblem.is_good_state or if it is the default. This is useful for checks in amplitude estimators, such as AmplitudeEstimation, which only support the default implementation.

• Adds a flag local to the ComputeUncompute state fidelity class that allows to compute the local fidelity, which is defined by averaging over single-qubit projectors.

• Gradient classes rearrange the gradient result according to the order of the input parameters now.

  Example:

  from qiskit.algorithms.gradients import ParamShiftEstimatorGradient
  from qiskit.circuit import QuantumCircuit, Parameter
  from qiskit.primitives import Estimator
  from qiskit.quantum_info import SparsePauliOp
  
  # Create a circuit with a parameter
  p = {i: Parameter(f'p{i}') for i in range(3)}
  qc = QuantumCircuit(1)
  qc.rx(p[0], 0)
  qc.ry(p[1], 0)
  qc.rz(p[2], 0)
  op = SparsePauliOp.from_list([("Z", 1)])
  param_values = [0.1, 0.2, 0.3]
  
  # Create a gradient object
  estimator = Estimator()
  grad = ParamShiftEstimatorGradient(estimator)
  result = grad.run(qc, op, [param_values]).result()
  # would produce a gradient of the form [df/dp0, df/dp1, df/dp2]
  result = grad.run(qc, op, [param_values], parameters=[[p[2], p[0]]]).result()
  # would produce a gradient of the form [df/dp2, df/dp0]
  

• Added support for handling time-dependent Hamiltonians (i.e. singly parametrized operators) to the TrotterQRTE class. To facilitate working with this, added the num_timesteps attribute and a matching keyword argument to the TrotterQRTE constructor to control the number of time steps to divide the full evolution.

• Added support for observable evaluations at every time-step during the execution of the TrotterQRTE class. The TimeEvolutionProblem.aux_operators is evaluated at every time step if the ProductFormula.reps attribute of the input product_formula argument in the constructor is set to 1.

• Added extensions to the VQD algorithm, which allow to pass a list of optimizers and initial points for the different minimization runs. For example, the k-th initial point and k-th optimizer will be used for the optimization of the k-1-th exicted state.

Quantum Information Features

• Added two new constructor methods, Clifford.from_matrix() and Clifford.from_operator(), that create a Clifford object from its unitary matrix and operator representation respectively.

• The constructor of Clifford now can take any Clifford gate object up to 3 qubits as long it implements a to_matrix method, including parameterized gates such as Rz(pi/2), which were not convertible before.

• Added new utility functions: commutator(), anti_commutator(), and double_commutator() which are used to compute commutators for any object implementing the LinearOp abstract base class such as QuantumChannel, SparsePauliOp, or ScalarOp.

• Added the method StabilizerState.equiv, that checks if the generating sets of two stabilizer states generate the same stabilizer group. For example, the stabilizer group of the two-qubit Bell state contains the four elements \(\{II, XX, -YY, ZZ\}\) and hence can be generated by either \([XX, ZZ]\), \([XX, -YY]\) or \([-YY, ZZ]\).

• Added a new method, partial_transpose(), to the qiskit.quantum_info module’s DensityMatrix class. This method is used to compute the partial transposition of a density matrix, which is necessary for detecting entanglement between bipartite quantum systems.

• Added a method qiskit.quantum_info.Operator.apply_permutation() that pre-composes or post-composes an Operator with a Permutation. This method works for general qudits.

  Here is an example to calculate \(P^\dagger.O.P\) which reorders Operator’s bits:

  import numpy as np
  from qiskit.quantum_info.operators import Operator
  
  op = Operator(np.array(range(576)).reshape((24, 24)), input_dims=(2, 3, 4), output_dims=(2, 3, 4))
  perm = [1, 2, 0]
  inv_perm = [2, 0, 1]
  conjugate_op = op.apply_permutation(inv_perm, front=True).apply_permutation(perm, front=False)
  

  The conjugate operator has dimensions (4, 2, 3) x (4, 2, 3), which is consistent with permutation moving qutrit to position 0, qubit to position 1, and the 4-qudit to position 2.

• Natively support the construction of SparsePauliOp objects with ParameterExpression coefficients, without requiring the explicit construction of an object-array. Now the following is supported:

  from qiskit.circuit import Parameter
  from qiskit.quantum_info import SparsePauliOp
  
  x = Parameter("x")
  op = SparsePauliOp(["Z", "X"], coeffs=[1, x])
  

• Added the SparsePauliOp.assign_parameters() method and SparsePauliOp.parameters attribute to assign and query unbound parameters inside a SparsePauliOp. This function can for example be used as:

  from qiskit.circuit import Parameter
  from qiskit.quantum_info import SparsePauliOp
  
  x = Parameter("x")
  op = SparsePauliOp(["Z", "X"], coeffs=[1, x])
  
  # free_params will be: ParameterView([x])
  free_params = op.parameters
  
  # assign the value 2 to the parameter x
  bound = op.assign_parameters([2])
  

Pulse Features

• Added new SymbolicPulse classes to the pulse library (qiskit.pulse.library) The new pulses in the library are:

  • Sin

  • Cos

  • Sawtooth

  • Triangle

  These new classes are instances of ScalableSymbolicPulse. With the exception of the Sawtooth phase, behavior is identical to that of the corresponding waveform generator function (e.g. sin()). The phase for the Sawtooth class is defined such that a phase of \(2\pi\) shifts by a full cycle.

• Added support to QPY (qiskit.qpy) for working with pulse ScheduleBlock instances with unassigned references, and preserving the data structure for the reference to subroutines. This feature allows users to serialize and deserialize a template pulse program for tasks such as pulse calibration. For example:

  from qiskit import pulse
  from qiskit import qpy
  
  with pulse.build() as schedule:
      pulse.reference("cr45p", "q0", "q1")
      pulse.reference("x", "q0")
      pulse.reference("cr45p", "q0", "q1")
  
  with open('template_ecr.qpy', 'wb') as fd:
      qpy.dump(schedule, fd)
  

• A new method CalibrationEntry.user_provided() has been added to calibration entries. This method can be called to check whether the entry is defined by an end user or backend.

• Added a new method Target.get_calibration() which provides convenient access to the calibration of an instruction in a Target object This method can be called with parameter args and kwargs, and it returns a pulse schedule built with parameters when the calibration is templated with parameters.

Providers Features

• The BackendV2Converter class has a new keyword argument, filter_faulty, on its constructor. When this argument is set to True the converter class will filter out any qubits or operations listed as non-operational in the BackendProperties payload for the input BackendV1. While not extensively used a BackendProperties object supports annotating both qubits and gates as being non-operational. Previously, if a backend had set that flag on any qubits or gates the output BackendV2 instance and its Target would include all operations whether they were listed as operational or not. By leveraging the new flag you can filter out these non-operational qubits and gates from the Target. When the flag is set the output backend will still be listed as the full width (e.g. a 24 qubit backend with 4 qubits listed as not operational will still show it has 24 qubits) but the faulty qubits will not have any operations listed as being supported in the Target.

• The Options class now implements the the Mapping protocol and __setitem__ method. This means that Options instances now offer the same interface as standard dictionaries, except for the deletion methods (__delitem__, pop, clear). Key assignments are validated by the validators, if any are registered.

Visualization Features

• Added a new function, staged_pass_manager_drawer(), which is used for visualizing a StagedPassManager instance. It draws the full pass manager with each stage represented as an outer box.

  For example:

  from qiskit.visualization import staged_pass_manager_drawer
  from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
  from qiskit.providers.fake_provider import FakeSherbrooke
  
  backend = FakeSherbrooke()
  pm = generate_preset_pass_manager(3, backend)
  staged_pass_manager_drawer(pm)
  

• The StagedPassManager.draw() method has been updated to include visualization of the stages in addition to the overall pass manager. The stages are represented by outer boxes in the visualization. In previous releases the stages were not included in the visualization. For example:

  from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
  from qiskit.providers.fake_provider import FakeSherbrooke
  
  backend = FakeSherbrooke()
  pm = generate_preset_pass_manager(3, backend)
  pm.draw(pm)
  

• Added a new keyword argument, figsize, to the plot_bloch_multivector() function. This argument can be used to set a size for individual Bloch sphere sub-plots. For example, if there are \(n\) qubits and figsize is set to (w, h), then the overall figure width is set to \(n \cdot w\), while the overall height is set to \(h\).

• Added a new keyword argument, font_size, to the plot_bloch_multivector() function. This argument can be used to control the font size in the output visualization.

• Added two new keyword arguments, title_font_size and title_pad, to the plot_bloch_multivector() function. These arguments can be used to control the font size of the overall title and its padding respectively.

Upgrade Notes

• The minimum supported Rust version (MSRV) has been increased from 1.56.1 to 1.61.0. If you’re are building Qiskit from source you will now need to ensure that you have at least Rust 1.61.0 installed to be able to build Qiskit. This change was made because several upstream dependencies have increased their MSRVs.

• Removed the usage of primitives with the context manager and the initialization with circuits, (observables only for Estimator), and parameters which was deprecated in the Qiskit Terra 0.22.0 release in October 2022.

• PrimitiveJob.submit() no longer blocks on execution finishing. As a result, Sampler.run(), BackendSampler.run(), Estimator.run() and BaseEstimator.run() do not block until PrimitiveJob.result() method is called.

Transpiler Upgrade Notes

• The maximum number of trials evaluated when searching for the best layout using VF2Layout and VF2PostLayout is now limited in level_1_pass_manager(), level_2_pass_manager(), and level_3_pass_manager() to 2 500, 25 000, and 250 000, respectively. Previously, all found possible layouts were evaluated. This change was made to prevent transpilation from hanging during layout scoring for circuits with many connected components on larger devices, which scales combinatorially since each connected component would be evaluated in all possible positions on the device. To perform a full search as before, manually run VF2PostLayout over the transpiled circuit in strict mode, specifying 0 for max_trials.

• The previously deprecated condition attribute of the DAGDepNode class has been removed. It was marked as deprecated in the 0.18 release (07-2021). Instead you should use the condition attribute of the op attribute to access the condition of an operation node. For other node types there is no condition to access.

• The default value of metadata in both DAGCircuit and DAGDependency has been changed from None to {} for compatibility with the matching metadata attribute of QuantumCircuit.

• The CouplingMap.__eq__`() method has been updated to check that the edge lists of the underlying graphs contain the same elements. Under the assumption that the underlying graphs are connected, this check additionally ensures that the graphs have the same number of nodes with the same labels. Any code using CouplingMap() == CouplingMap() to check object equality should be updated to CouplingMap() is CouplingMap().

• When running the transpile() function with a BackendV1 based backend or a BackendProperties via the backend_properties keyword argument that has any qubits or gates flagged as faulty the function will no longer try to automatically remap the qubits based on this information. The method by which transpile() attempted to do this remapping was fundamentally flawed and in most cases of such a backend it would result an internal error being raised. In practice very few backends ever set the fields in BackendProperties to flag a qubit or gate as faulty. If you were relying on transpile() to do this re-mapping for you, you will now need to manually do that and pass a mapped input to the coupling_map and backend_properties arguments which has filtered out the faulty qubits and gates and then manually re-map the output.

• The result of transpilations for fixed seeds may have changed compared to previous versions of Qiskit Terra. This is because of internal tweaks to the routing algorithm used by SabreSwap and SabreLayout, which are the default routing and layout passes respectively, to make them significantly faster for large circuits.

Circuits Upgrade Notes

• The QuantumCircuit metadata attribute now always returns a dictionary, and can only be set to a dictionary. Previously, its default value was None, and could be manually set to None or a dictionary.

Algorithms Upgrade Notes

• The deprecated modules factorizers and linear_solvers, containing HHL and Shor have been removed from qiskit.algorithms. These functionalities were originally deprecated as part of the 0.22.0 release (released on October 13, 2022). You can access the code through the Qiskit Textbook instead: Linear Solvers (HHL) , Factorizers (Shor)

Pulse Upgrade Notes

• Target.update_from_instruction_schedule_map() no longer raises KeyError nor ValueError when qubits are missing in the target instruction or inst_name_map is not provided for the undefined instruction. In the former case, it just ignores the input InstructionScheduleMap's definition for undefined qubits. In the latter case, a gate mapping is pulled from the standard Qiskit gates and finally, a custom opaque Gate object is defined from the schedule name if no mapping is found.

Providers Upgrade Notes

• The deprecated max_credits argument to execute(), assemble() and all of the Qobj configurations (e.g. QasmQobjConfig and PulseQobjConfig) has been removed. This argument dates back to early versions of Qiskit which was tied more closely to the IBM Quantum service offering. At that time the max_credits field was part of the « credit system » used by IBM Quantum’s service offering. However, that credit system has not been in use on IBM Quantum backends for nearly three years and also Qiskit is not tied to IBM Quantum’s service offerings anymore (and hasn’t been for a long time). If you were relying on this option in some way for a backend you will need to ensure that your BackendV2 implementation exposes a max_credits field in its Options object.

• The name attribute on the BackendV2 based fake backend classes in qiskit.providers.fake_provider have changed from earlier releases. Previously, the names had a suffix "_v2" to differentiate the class from the BackendV1 version. This suffix has been removed as having the suffix could lead to inconsistencies with other snapshotted data used to construct the backend object.

Deprecation Notes

• The modules qiskit.opflow, qiskit.utils.backend_utils, qiskit.utils.mitigation, qiskit.utils.measurement_error_mitigation, class qiskit.utils.QuantumInstance and methods find_regs_by_name(), run_circuits() have been deprecated and will be removed in a future release. Using QuantumInstance is superseded by BaseSampler. See Opflow Migration. See QuantumInstance Migration.

Transpiler Deprecations

• The transpiler routing pass, BIPMapping has been deprecated and will be removed in a future release. It has been replaced by an external plugin package: qiskit-bip-mapper. Details for this new package can be found at the package’s github repository:

  https://github.com/qiskit-community/qiskit-bip-mapper

  The pass was made into a separate plugin package for two reasons, first the dependency on CPLEX makes it harder to use and secondly the plugin packge more cleanly integrates with transpile().

• Misspelled aquire_alignment in the class Target has been replaced by correct spelling acquire_alignment. The old constructor argument aquire_alignment and Target.aquire_alignment are deprecated and will be removed in a future release. Use Target.acquire_alignment instead to get and set the alignment constraint value.

Circuits Deprecations

• Setting the QuantumCircuit metadata attribute to None has been deprecated and will no longer be supported in a future release. Instead, users should set it to an empty dictionary if they want it to contain no data.

Algorithms Deprecations

• All of the following features are now deprecated, after having been made pending deprecation since 0.22.0. More information is available at https://qisk.it/algo_migration.

  • Module qiskit.algorithms.minimum_eigen_solvers is deprecated and superseded by qiskit.algorithms.minimum_eigensolvers.

  • Module qiskit.algorithms.eigen_solvers is deprecated and superseded by qiskit.algorithms.eigensolvers.

  • Module qiskit.algorithms.evolvers is deprecated and superseded by qiskit.algorithms.time_evolvers.

  • Class qiskit.algorithms.TrotterQRTE is deprecated and superseded by qiskit.algorithms.time_evolvers.trotterization.TrotterQRTE.

  • Using QuantumInstance or Backend is deprecated and superseded by BaseSampler in the following classes:

    • Grover

    • AmplitudeEstimation

    • FasterAmplitudeEstimation

    • IterativePhaseEstimation

    • MaximumLikelihoodAmplitudeEstimation

    • HamiltonianPhaseEstimation

    • IterativePhaseEstimation

    • PhaseEstimation

  • Using QuantumInstance or Backend or ExpectationBase is deprecated and superseded by BaseSampler in the following static method: get_fidelity()

  • Function eval_observables() is deprecated and superseded by estimate_observables() function.

Quantum Information Deprecations

• The PauliTable and StabilizerTable are deprecated and will be removed in a future release. Instead, the PauliList should be used. With this change, table() has been deprecated so that you should operate directly from tableau() without it.

Pulse Deprecations

• Assignment of complex values to ParameterExpression in any Qiskit Pulse object now raises a PendingDeprecationWarning. This will align the Pulse module with other modules where such assignment wasn’t possible to begin with. The typical use case for complex parameters in the module was the SymbolicPulse library. As of Qiskit-Terra 0.23.0 all library pulses were converted from complex amplitude representation to real representation using two floats (amp,angle), as used in the ScalableSymbolicPulse class. This eliminated the need for complex parameters. Any use of complex parameters (and particularly custom-built pulses) should be converted in a similar fashion to avoid the use of complex parameters.

Bug Fixes

• The AmplitudeEstimation class now correctly warns if an EstimationProblem with a set is_good_state property is passed as input, as it is not supported and ignored. Previously, the algorithm would silently ignore this option leading to unexpected results.

• QuantumCircuit.append() will now correctly raise an error if given an incorrect number of classical bits to apply to an operation. Fix #9385.

• The BarrierBeforeFinalMeasurements and MergeAdjacentBarriers transpiler passes previously had a non-deterministic order of their emitted Barrier instructions. This did not change the semantics of circuits but could, in limited cases where there were non-full-width barriers, cause later stochastic transpiler passes to see a different topological ordering of the circuit and consequently have different outputs for fixed seeds. The passes have been made deterministic to avoid this.

• The return type of run() will now always be the same as that of its first argument. Passing a single circuit returns a single circuit, passing a list of circuits, even of length 1, returns a list of circuits. See #9798.

• Fixed a bug where PauliOp.adjoint() did not return a correct value for Paulis with complex coefficients, like PauliOp(Pauli("iX")). Fixed #9433.

• Fixed an issue with the circuit drawer function circuit_drawer() and QuantumCircuit.draw() method when displaying instruction parameters that type QuantumCircuit which would result in an illegible drawing. Fixed #9908

• Fixed an issue with the circuit drawer function circuit_drawer() and QuantumCircuit.draw() method when using the text method and the argument vertical_compression="low" where it would use an incorrect character for the top-right corner of boxes used to represent gates in the circuit.

• Fixed an issue with the Gate.control() method where it previously would incorrectly handle str or None input types for the ctrl_state argument.

• Fixed an edge case in the construction of Pauli instances; a string with an optional phase and no qubits is now a valid label, making an operator with no qubits (such as Pauli("-i")). This was already possible when using the array forms, or empty slices. Fixed #9720.

• Fixed an issue when using the pulse macro measure() when working with a BackendV2 based backend. Previously, trying to use qiskit.pulse.macros.measure() with a BackendV2 based backend would have resulted in an error. Fixed #9488

• Fixed an issue with the marginal_distribution() function where it would incorrectly raise an error when an input counts dictionary was using a numpy integer type instead of the Python int type. The underlying function always would handle the different types correctly, but the input type checking was previously incorrectly raising a TypeError in this case.

• Fixed a bug where Parameter.is_real() did not return None when the parameter is not bound. Fixed #8619.

• Circuits containing C3SXGate can now be output and read in again safely from the OpenQASM 2.0 exporter (QuantumCircuit.qasm()) and parser (QuantumCircuit.from_qasm_str()).

• Fixed a bug in QPY (qiskit.qpy) where circuits containing gates of class MCXGate, MCXGrayCode, and MCXRecursive, and MCXVChain would fail to serialize. See #9390.

• Fixed the transpiler routing passes StochasticSwap, SabreSwap, LookaheadSwap, and BasicSwap so that they consistently raise a TranspilerError when their respective .run() method is called if the passes were initialized with coupling_map=None. Previously, these passes would raise errors in this case but they were all caused by side effects and the specific exception was not predictable. Fixed #7127

• Manually setting an item in QuantumCircuit.data will now correctly allow the operation to be any object that implements Operation, not just a circuit.Instruction. Note that any manual mutation of QuantumCircuit.data is discouraged; it is not usually any more efficient than building a new circuit object, as checking the invariants surrounding parametrised objects can be surprisingly expensive.

• Fixed a bug when constructing DAGDependency from within the TemplateOptimization transpiler pass, which could lead to incorrect optimizations.

• Fixed a bug in TensoredOp.to_matrix() where the global coefficient of the operator was multiplied to the final matrix more than once. Now, the global coefficient is correclty applied, independent of the number of tensored operators or states. Fixed #9398.

• Fixed global-phase handling in the UnrollCustomDefinitions transpiler pass if the instruction in question had a global phase, but no instructions in its definition field.

• Fixed the the type annotations for the transpile() function. The return type is now narrowed correctly depending on whether a single circuit or a list of circuits was passed.

• Fixed a bug where IterativePhaseEstimation was generating the wrong circuit, causing the algorithm to fail for simple cases. Fixed #9280.

• A bug has been fixed which had allowed broadcasting when a PauliList is initialized from Paulis or labels. For instance, the code PauliList(["XXX", "Z"]) now raises a ValueError rather than constructing the equivalent of PauliList(["XXX", "ZZZ"]).

• The OpenQASM 2 exporter (QuantumCircuit.qasm()) will no longer emit duplicate definitions for gates that appear in other gates” definitions. See #7771, #8086, #8402, #8558, and #9805.

• The OpenQASM 2 exporter (QuantumCircuit.qasm()) will now handle multiple and nested definitions of UnitaryGate. See #4623, #6712, #7772, and #8222.

• The OpenQASM 2 exporter (QuantumCircuit.qasm()) will now output definitions for gates used only in other gates” definitions in a correct order. See #7769 and #7773.

• Standard gates defined by Qiskit, such as RZXGate, will now have properly parametrised definitions when exported using the OpenQASM 2 exporter (QuantumCircuit.qasm()). See #7172.

• Quantum volume circuits (QuantumVolume) are now supported by the OpenQASM 2 exporter (QuantumCircuit.qasm()). See #6466 and #7051.

• The OpenQASM 2 exporter will now output gates with no known definition with opaque statements, rather than failing. See #5036.

• An issue that prevented transpile() from working when passed a list of CouplingMap objects was fixed. Note that passing such a list of coupling maps is deprecated and will not be possible starting with Qiskit Terra 0.25. Fixes #9885.

• Previous to this release, the figsize argument of plot_bloch_multivector() was not used by the visualization, making it impossible to change its size (e.g. to shrink it for single-qubit states). This release fixes it by introducing a use for the figsize argument.

• Fixed an issue in transpile() with optimization_level=1 (as well as in the preset pass managers returned by generate_preset_pass_manager() and level_1_pass_manager()) where previously if the routing_method and layout_method arguments were not set and no control flow operations were present in the circuit then in cases where routing was required the VF2PostLayout transpiler pass would not be run. This was the opposite of the expected behavior because VF2PostLayout is intended to find a potentially better performing layout after a heuristic layout pass and routing are run. Fixed #9936

• Construction of a Statevector from a QuantumCircuit containing zero-qubit operations will no longer raise an error. These operations impart a global phase on the resulting statevector.

• Fixed an issue in tranpiler passes for padding delays, which did not respect target’s constraints and inserted delays even for qubits not supporting Delay instruction. PadDelay and PadDynamicalDecoupling are fixed so that they do not pad any idle time of qubits such that the target does not support Delay instructions for the qubits. Also legacy scheduling passes ASAPSchedule and ALAPSchedule, which pad delays internally, are fixed in the same way. In addition, transpile() is fixed to call PadDelay with a target object so that it works correctly when called with scheduling_method option. Fixed #9993

• Fixed the type annotations on the QuantumCircuit.assign_parameters() method to correctly reflect the change in return type depending on the value of the inplace argument.

• Fixed a performance scaling issue with the VF2Layout and VF2PostLayout passes in the preset pass managers and transpile(), which would occur when transpiling circuits with many connected components on large devices. Now the transpiler passes set upper bounds on the number of potential layouts that will be evaluated.

• Fixed an issue in the state_to_latex() function where it would potentially produce invalid LaTeX due to unintended coefficient rounding. This could also result in errors when the state_drawer() was called. Fixed #9297.

Aer 0.12.0

No change

IBM Q Provider 0.20.2

No change

Qiskit 0.42.1

Terra 0.23.3

Prelude

Qiskit Terra 0.23.3 is a minor bugfix release.

Bug Fixes

• Fixes a bug in the Optimize1qGatesDecomposition transformation pass where the score for substitutions was wrongly calculated when the gate errors are zero.

• The method ECRGate.inverse() now returns another ECRGate instance rather than a custom gate, since it is self inverse.

• Clip probabilities in the QuantumState.probabilities() and QuantumState.probabilities_dict() methods to the interval [0, 1]. This fixes roundoff errors where probabilities could e.g. be larger than 1, leading to errors in the shot emulation of the sampler. Fixed #9761.

• Fixed a bug in the BackendSampler where the binary probability bitstrings were truncated to the minimal number of bits required to represent the largest outcome as integer. That means that if e.g. {"0001": 1.0} was measured, the result was truncated to {"1": 1.0}.

• Fixed an issue with the PassManagerConfig.from_backend() constructor method when it was used with a BackendV1 based simulator backend. For some simulator backends which did not populate some optional fields the constructor would error. Fixed #9265 and #8546

• Fixed the BackendSampler and BackendEstimator to run successfully with a custom bound_pass_manager. Previously, the execution for single circuits with a bound_pass_manager would raise a ValueError because a list was not returned in one of the steps.

• The GateDirection transpiler pass will no longer reject gates that have been given explicit calibrations, but do not exist in the generic coupling map or target.

• Fixed an issue with the CommutationChecker class where it would attempt to internally allocate an array for \(2^{n}\) qubits when it only needed an array to represent \(n\) qubits. This could cause an excessive amount of memory for wide gates, for example a 4 qubit gate would require 32 gigabytes instead of 2 kilobytes. Fixed #9197

• Getting empty calibration from InstructionProperties raises AttributeError has been fixed. Now it returns None.

• Fixed qasm() so that it appends ; after reset instruction.

• Register and parameter names will now be escaped during the OpenQASM 3 export (qasm3.dumps()) if they are not already valid identifiers. Fixed #9658.

• QPY (using qpy.load()) will now correctly deserialize StatePreparation instructions. Previously, QPY would error when attempting to load a file containing one. Fixed #8297.

• Fixed a bug in random_circuit() with 64 or more qubits and conditional=True, where the resulting circuit could have an incorrectly typed value in its condition, causing a variety of failures during transpilation or other circuit operations. Fixed #9649.

• Fixed an issue with the OneQubitEulerDecomposer class’s methods angles() and angles_and_phase() would error if the input matrix was of a dtype other than complex/np.cdouble. In earlier releases this worked fine but this stopped working in Qiskit Terra 0.23.0 when the internals of OneQubitEulerDecomposer were re-written in Rust. Fixed #9827

• The Qiskit gates CCZGate, CSGate, CSdgGate are not defined in qelib1.inc and, therefore, when dump as OpenQASM 2.0, their definition should be inserted in the file. Fixes #9559, #9721, and #9722.

Aer 0.12.0

No change

IBM Q Provider 0.20.2

No change

Qiskit 0.42.0

Terra 0.23.2

No change

Aer 0.12.0

Prelude

The Qiskit Aer 0.12.0 release highlights are:

• Added a new GPU tensor network simulator based on cuTensorNet

• Added a new AerDensityMatrix class to the qiskit_aer.quantum_info module

• Greatly improving the runtime performance of the AerSimulator and the legacy QasmSimulator, StatevectorSimulator, and UnitarySimulator classes by directly converting the input QuantumCircuit objects to an internal C++ representation instead of first serializing the circuit to a QasmQobj. This improvement will be most noticeable for circuits with a small number of qubits or parameterized circuits using the parameter_binds keyword argument.

New Features

• Added a new class method from_backend_properties() to the NoiseModel. This enables constructing a new NoiseModel from a BackendProperties object. Similar functionality used to be present in the NoiseModel.from_backend() constructor, however it was removed since a BackendProperties object alone doesn’t contain sufficient information to create a NoiseModel object.

• Added a new class, AerDensityMatrix, to the qiskit_aer.quantum_info module. This class is used to provide the same interface to the upstream DensityMatrix class in Qiskit but backed by Qiskit Aer’s simulation.

• Added a new keyword argument, abelian_grouping, to the Estimator. This argument is used to control whether the Estimator will group the input observables into qubit-wise commutable observables which reduces the number of circuit executions required to compute the expectation value and improves the runtime performance of the Estimator. By default this is set to True.

• AerState has a new method initialize_density_matrix() that sets a density matrix to AER::QV::DensityMatrix. This method will be called in q.i.states.DensityMatrix to initialize its data with ndarray. initialize_density_matrix() has a boolean argument that specifies copy or share of ndarray data. If the data is shared with C++ and python, the data must not be collected in python while C++ accesses it.

• The overhead for running simulations with run() (for all simulator backend classess) has been greatly reduced. This was accomplished by no longer internally serializing QuantumCircuit objects into QasmQobj and instead the QuantumCircuit object directly to an internal C++ circuit structure used for simulation. This improvement is most noticeable for simulations of circuts with a small number of qubits or parameterized circuits using the parameter_binds keyword argument of run(). Note that pulse simualation (via the now deprecated PulseSimulator) and DASK-based simulation still use the internal serialization and will not see this performance improvement.

• Added a new method to the AerJob, circuits(), which returns a list of QuantumCircuit objects. This method returns None if Qobj is used for simulation.

• AerState and AerStatevector now support applying Kraus operators. In AerStatevector, one of the Kraus operators is applied randomly to the quantum state based on the error probabilities.

• Added a new simulation method based on NVIDIA’s cuTensorNet APIs of cuQuantum SDK. This provides a GPU accelerated general tensor network simulator that can simulate any quantum circuit, by internally translating the circuit into a tensor network to perform the simulation. To use this simulation method, set method="tensor_network" and device="GPU" when initializing an AerSimulator object. For example:

  from qiskit_aer import AerSimulator
  
  tensor_net_sim = AerSimulator(method="tensor_network", device="GPU")
  

  This method supports both statevector and density matrix simulations. Noise simulation can also be done with a density matrix single shot simulation if there are not any SaveStatevector operations in the circuit.

  This new simulation method also supports parallelization with multiple GPUs and MPI processes by using tensor network slicing technique. However, this type of simulation will likely take a very long time if the input circuits are complicated.

• The BLA_VENDOR environment variable can now be specified to use a different BLAS library when building Qiskit Aer from source. By default if this is not specified OpenBLAS will be used by default. If the BLAS library specified in BLA_VENDOR` can not be found then the Cmake build process will stop.

Known Issues

• This release of Qiskit Aer is not compatible with the Conan 2.X release series. If you are building Qiskit Aer from source manually ensure that you are using a Conan 1.x release. Compatibility with newer versions of Conan will be fixed in a future release. You can refer to issue #1730 for more details.

Upgrade Notes

• The default behavior of the Estimator primitive will now group the input observable into qubit-wise commutable observables. The grouping reduces the number of circuits to be executed and improves the performance. If you desire the previous behavior you can initialize your Estimator instance with the keyword argument abelian_grouping=False.

• Removed the usage of primitives with the context manager and the initialization with circuits, (observables only for Estimator), and parameters which has been deprecated in the Qiskit Terra 0.22.0 release in October 2022.

• The behavior of run() method has changed when invalid or otherwise unsimulatable QuantumCircuit objects are passed as an input. Previously, in these cases the run() method would return an AerJob whose result() method would return a Result with the ERROR or PARTIAL COMPLETED (depending on whether all the circuit inputs or only some were invalid or not). Starting in this release instead of returning a result object with these statuses an exception will be raised instead. This change was necessary because of the performance improvements by no longer internally serializing the QuantumCircuit objects to a Qobj before passing it to C++, instead the direct conversion from QuantumCircuit now errors directly when trying to simulate a circuit Qiskit Aer is unable to execute. If you desire the previous behavior you can build Qiskit Aer in standalone mode and manually serialize your QuantumCircuit objects to a JSON representation of the QasmQobj which you then pass to the standalone Aer binary which will retain the previous behavior.

• A deprecated method add_nonlocal_quantum_error() in NoiseModel has been removed. No alternative method is available. If you want to add non-local quantum errors, you should write a transpiler pass that inserts your own quantum error into a circuit, and run the pass just before running the circuit on Aer simulator.

• The NoiseModel.from_backend() now has changed not to accept BackendProperties object as a backend argument. Use newly added NoiseModel.from_backend_properties() method instead.

• A deprecated standard_gates argument broadly used in several methods and functions (listed below) across noise module has been removed.

  • NoiseModel.from_backend() and noise.device.basic_device_gate_errors()

  • kraus_error(), mixed_unitary_error(), pauli_error() and depolarizing_error() in noise.errors.standard_errors

  • QuantumError.__init__()

  No alternative means are available because the user should be agnostic about how the simulator represents noises (quantum errors) internally.

• The constructor of QuantumError has now dropped the support of deprecated json-like input for noise_ops argument. Use the new styple input for noise_ops argument instead, for example,

  from qiskit.circuit.library import IGate, XGate
  from qiskit_aer.noise import QuantumError
  
  error = QuantumError([
      ((IGate(), [1]), 0.9),
      ((XGate(), [1]), 0.1),
  ])
  
  # json-like input is no longer accepted (the following code fails)
  #  error = QuantumError([
  #      ([{"name": "I", "qubits": [1]}], 0.9),
  #      ([{"name": "X", "qubits": [1]}], 0.1),
  #  ])
  

  Also it has dropped deprecated arguments:

  • number_of_qubits: Use QuantumCircuit to define noise_ops instead.

  • atol: Use QuantumError.atol attribute instead.

  • standard_gates: No alternative is available (users should not too much care about internal representation of quantum errors).

• The deprecated noise.errors.errorutils module has been entirely removed and no alternatives are available. All functions in the module were helper functions meant to be used only for implementing functions in standard_errors (i.e. they should have been provided as private functions) and no longer used in it.

• The deprecated utils.noise_remapper have been entirely removed and no alternatives are available since the C++ code now automatically truncates and remaps noise models if it truncates circuits.

• All deprecated functions (pauli_operators() and reset_operators()) and class (NoiseTransformer) in utils.noise_transformation module have been removed, and no alternatives are available. They were in fact private functions/class used only for implementing approximate_quantum_error() and should not have been public.

• The previously deprecated qobj argument name of the AerSimulator and PulseSimulator classes” run() method has now been removed. This argument name was deprecated as part of the Qiskit Aer 0.8.0 release and has been by the circuits and schedules argument name respectively.

• Aer’s setup.py has been updated to no longer attempt to make calls to pip to install build requirements, both manually and via the setup_requires option in setuptools.setup. The preferred way to build Aer is to use a PEP 517-compatible builder such as:

  pip install .
  

  This change means that a direct call to setup.py will no longer work if the build requirements are not installed. This is inline with modern Python packaging guidelines.

Deprecation Notes

• Support for running Qiskit Aer with Python 3.7 support has been deprecated and will be removed in a future release. This means starting in a future release you will need to upgrade the Python version you’re using to Python 3.8 or above.

• The PulseSimulator backend has been deprecated and will be removed in a future release. If you’re using the PulseSimulator backend to perform pulse level simulation, instead you should use the Qiskit Dynamics library instead to perform the simulation. Qiskit Dynamics provides a more flexible and robust pulse level simulation framework than the PulseSimulator backend.

• The qobj() method of the AerJob class is now deprecated and will be removed in a future release. The use of the qobj format as input to run() has been deprecated since qiskit-aer 0.9.0 and in most cases this method would return None now anyway. If you’d like to get the input to the run() method now you can use the circuits() method instead, which will return the QuantumCircuit objects that were simulated in the job.

• A warnings argument broadly used in several methods and functions across noise module has been deprecated in favor of the use of filtering functions in Python’s standard warnings library.

Bug Fixes

• Fixed an issue when creating a new AerStatevector instance from a numpy.ndarray that had non-contiguous memory. Previously, this would result in unexpected behavior (and a potential error) as the AerStatevector assumed the input array was contiguous. This has been fixed so that memory layout is checked and the numpy.ndarray will be copied internally as a contiguous array before using it.

• Fixed an issue with the Sampler class where it would previously fail if the input QuantumCircuit contained multiple multiple classical registers. Fixed #1679

• The bits count of classical register used on the GPU was not set before calculating free available memory for chunks that causes infinite loop. So this fix set bits count before allocating chunks if batch shots execution is enabled.

• Fix build errors and test errors when enabling GPU but disabling cuQuantum.

• Fixed an issue in the matrix product state simulation method (i.e. setting the keyword argument method="matrix_product_state" when initializing an AerSimulator object) where the simulator would incorrectly sort the qubits prior to performing measurment potentially resulting in an infinite loop. This has been fixed so the measurement of the qubits occurs in the order of the current MPS structure and then sorting afterwards as a post-processing step. This also will likely improve the performance of the simulation method and enable more accurate representation of entangled states. Fixed #1694

• The AerSimulator backend with methods:

  • statevector

  • density_matrix

  • matrix_product_state

  • stabilizer

  now report that they support break_loop and continue_loop instructions when used as backends for the Terra transpile() function. The simulators already did support these, but had just not been reporting it.

IBM Q Provider 0.20.2

This release removes the overly restrictive version constraints set in the requirements for the package added in 0.20.1. For the 0.20.1 the only dependency that was intended to have a version cap was the requests-ntlm package as its new release was the only dependency which currently has an incompatibility with qiskit-ibmq-provider. The other version caps which were added as part of 0.20.1 were causing installation issues in several environments because it made the qiskit-ibmq-provider package incompatible with the dependency versions used in other packages.

Qiskit 0.41.1

Terra 0.23.2

Prelude

The Qiskit Terra 0.23.2 patch release fixes further bugs identified in the 0.23 series.

Bug Fixes

• Add the following Clifford gates, that already exist in the circuit library, to the Clifford class: SXGate, SXdgGate, CYGate, DCXGate, iSwapGate and ECRGate.

• Add a decomposition of an ECRGate into Clifford gates (up to a global phase) to the standard equivalence library.

• Fixed an issue with the BackendV2Converter class when wrapping a BackendV1-based simulator. It would error if either the online_date field in the BackendConfiguration for the simulator was not present or if the simulator backend supported ideal implementations of gates that involve more than 1 qubit. Fixed #9562.

• Fixed an incorrect return value of the method BackendV2Converter.meas_map() that had returned the backend dt instead.

• Fixed missing return values from the methods BackendV2Converter.drive_channel(), measure_channel(), acquire_channel() and control_channel().

• The deprecated Qubit and Clbit properties register and index will now be correctly round-tripped by QPY (qiskit.qpy) in all valid usages of QuantumRegister and ClassicalRegister. In earlier releases in the Terra 0.23 series, this information would be lost. In versions before 0.23.0, this information was partially reconstructed but could be incorrect or produce invalid circuits for certain register configurations.

  The correct way to retrieve the index of a bit within a circuit, and any registers in that circuit the bit is contained within is to call QuantumCircuit.find_bit(). This method will return the correct information in all versions of Terra since its addition in version 0.19.

• Fixed an issue with the InstructionScheduleMap.has_custom_gate() method, where it would always return True when the InstructionScheduleMap object was created by Target. Fixed #9595.

• Fixed a bug in the NumPy-based eigensolvers (NumPyMinimumEigensolver / NumPyEigensolver) and in the SciPy-based time evolvers (SciPyRealEvolver / SciPyImaginaryEvolver), where operators that support conversion to sparse matrices, such as SparsePauliOp, were converted to dense matrices anyways.

• Fixed a bug in generate_basic_approximations() where the inverse of the SdgGate was not correctly recognized as SGate. Fixed #9585.

• Fixed a bug in the VQD algorithm where the energy evaluation function could not process batches of parameters, making it incompatible with optimizers with max_evals_grouped>1. Fixed #9500.

• Fixed bug in QNSPSA which raised a type error when the computed fidelities happened to be of type int but the perturbation was of type float.

Aer 0.11.2

No change

IBM Q Provider 0.20.1

Since qiskit-ibmq-provider is now deprecated, the dependencies have been bumped and fixed to the latest working versions. There was an issue with the latest version of the requests-ntlm package which caused some end to end tests to fail.

Qiskit 0.41.0

Terra 0.23.1

Prelude

Qiskit Terra 0.23.1 is a small patch release to fix bugs identified in Qiskit Terra 0.23.0

Bug Fixes

• An edge case of pickle InstructionScheduleMap with non-picklable iterable arguments is now fixed. Previously, using an unpickleable iterable as the arguments parameter to InstructionScheduleMap.add() (such as dict_keys) could cause parallel calls to transpile() to fail. These arguments will now correctly be normalized internally to list.

• Fixed a performance bug in ReverseEstimatorGradient where the calculation did a large amount of unnecessary copies if the gradient was only calculated for a subset of parameters, or in a circuit with many unparameterized gates.

• Fixed a bad deprecation of Register.name_format which had made the class attribute available only from instances and not the class. When trying to send dynamic-circuits jobs to hardware backends, this would frequently cause the error:

  AttributeError: 'property' object has no attribute 'match'
  

  Fixed #9493.

Aer 0.11.2

No change

IBM Q Provider 0.20.0

Prelude

This release of the qiskit-ibmq-provider package marks the package as deprecated and will be retired and archived in the future. The functionality in qiskit-ibmq-provider has been supersceded by 3 packages qiskit-ibm-provider, qiskit-ibm-runtime, and qiskit-ibm-experiment which offer different subsets of functionality that qiskit-ibmq-provider contained. You can refer to the table here:

https://github.com/Qiskit/qiskit-ibmq-provider#migration-guides

for links to the migration guides for moving from qiskit-ibmq-provider to its replacmeent packages.

Deprecation Notes

• As of version 0.20.0, qiskit-ibmq-provider has been deprecated with its support ending and eventual archival being no sooner than 3 months from that date. The function provided by qiskit-ibmq-provider is not going away rather it has being split out to separate repositories. Please see https://github.com/Qiskit/qiskit-ibmq-provider#migration-guides.

Bug Fixes

• In the upcoming terra release there will be a release candidate tagged prior to the final release. However changing the version string for the package is blocked on the qiskit-ibmq-provider right now because it is trying to parse the version and is assuming there will be no prelease suffix on the version string (see #8200 for the details). PR #1135 fixes this version parsing to use the regex from the pypa/packaging project which handles all the PEP440 package versioning include pre-release suffixes. This will enable terra to release an 0.21.0rc1 tag without breaking the qiskit-ibmq-provider.

• PR #1129 updates least_busy() method to no longer support BaseBackend as a valid input or output type since it has been long deprecated in qiskit-terra and has recently been removed.

• threading.currentThread and notifyAll were deprecated in Python 3.10 (October 2021) and will be removed in Python 3.12 (October 2023). PR #1133 replaces them with threading.current_thread, notify_all added in Python 2.6 (October 2008).

• Calls to run a quantum circuit with dynamic=True now raise an error that asks the user to install the new qiskit-ibm-provider.

Qiskit 0.40.0

This release officially deprecates the Qiskit IBMQ provider project as part of the Qiskit metapackage. This means that in a future release, pip install qiskit will no longer automatically include qiskit-ibmq-provider. If you’re currently installing or listing qiskit as a dependency to get qiskit-ibmq-provider, you should update to explicitly include qiskit-ibmq-provider as well. This is being done as the Qiskit project moves towards a model where the qiskit package only contains the common core functionality for building and compiling quantum circuits, programs, and applications. Packages that build on that core or link Qiskit to hardware or simulators will be installable as separate packages.

Terra 0.23.0

Prelude

Qiskit Terra 0.23.0 is a major feature release that includes a multitude of new features and bugfixes. The highlights for this release are:

• Support for importing OpenQASM 3 programs and creating QuantumCircuit objects from the input program via two new functions qiskit.qasm3.load() and qiskit.qasm3.loads().

• Improvements to the library of synthesis algorithms included in Qiskit. This includes the following new synthesis functions:

  • Clifford Synthesis

    • synth_clifford_layers()

    • synth_clifford_greedy()

  • Linear Function Synthesis:

    • synth_cnot_depth_line_kms()

    • synth_cnot_count_full_pmh()

  • Permutation Synthesis:

    • synth_permutation_basic()

    • synth_permutation_acg()

    • synth_permutation_depth_lnn_kms()

  • SolovayKitaevDecomposition detailed in: https://arxiv.org/abs/quant-ph/0505030

  • New plugins for HighLevelSynthesis:

    • ACGSynthesisPermutation

    • KMSSynthesisPermutation

    • BasicSynthesisPermutation

  • New plugin for UnitarySynthesis

    • SolovayKitaevSynthesis

• Performance improvements to SabreLayout. The pass is now primarily written in Rust which can lead to a runtime improvement, however the bigger improvement is in the quality of the output (on average, fewer SwapGate gates introduced by SabreSwap). For example, running SabreLayout and SabreSwap on Bernstein Vazirani circuits targeting the FakeSherbrooke backend yields the following results:

  (Source code)

  

This release also deprecates support for running with Python 3.7. A DeprecationWarning will now be emitted if you run Qiskit with Python 3.7. Support for Python 3.7 will be removed as part of the 0.25.0 release (currently planned for release in July 2023), at which point you will need Python 3.8 or newer to use Qiskit.

New Features

• The pulses in qiskit.pulse.library

  • Gaussian

  • GaussianSquare

  • Drag

  • Constant

  can be initialized with new parameter angle, such that two float parameters could be provided: amp and angle. Initialization with complex amp is still supported.

• The AdaptVQE class has a new attribute, eigenvalue_history, which is used to track the lowest achieved energy per iteration of the AdaptVQE. For example:

  from qiskit.algorithms.minimum_eigensolvers import VQE
  from qiskit.algorithms.minimum_eigensolvers.adapt_vqe import AdaptVQE
  from qiskit.algorithms.optimizers import SLSQP
  from qiskit.circuit.library import EvolvedOperatorAnsatz
  from qiskit.opflow import PauliSumOp
  from qiskit.primitives import Estimator
  from qiskit.quantum_info import SparsePauliOp
  from qiskit.utils import algorithm_globals
  
  excitation_pool = [
      PauliSumOp(
          SparsePauliOp(["IIIY", "IIZY"], coeffs=[0.5 + 0.0j, -0.5 + 0.0j]), coeff=1.0
      ),
      PauliSumOp(
          SparsePauliOp(["ZYII", "IYZI"], coeffs=[-0.5 + 0.0j, 0.5 + 0.0j]), coeff=1.0
      ),
      PauliSumOp(
          SparsePauliOp(
              ["ZXZY", "IXIY", "IYIX", "ZYZX", "IYZX", "ZYIX", "ZXIY", "IXZY"],
              coeffs=[
                  -0.125 + 0.0j,
                  0.125 + 0.0j,
                  -0.125 + 0.0j,
                  0.125 + 0.0j,
                  0.125 + 0.0j,
                  -0.125 + 0.0j,
                  0.125 + 0.0j,
                  -0.125 + 0.0j,
              ],
          ),
          coeff=1.0,
      ),
  ]
  ansatz = EvolvedOperatorAnsatz(excitation_pool, initial_state=self.initial_state)
  optimizer = SLSQP()
  h2_op = PauliSumOp.from_list(
      [
          ("IIII", -0.8105479805373266),
          ("ZZII", -0.2257534922240251),
          ("IIZI", +0.12091263261776641),
          ("ZIZI", +0.12091263261776641),
          ("IZZI", +0.17218393261915543),
          ("IIIZ", +0.17218393261915546),
          ("IZIZ", +0.1661454325638243),
          ("ZZIZ", +0.1661454325638243),
          ("IIZZ", -0.2257534922240251),
          ("IZZZ", +0.16892753870087926),
          ("ZZZZ", +0.17464343068300464),
          ("IXIX", +0.04523279994605788),
          ("ZXIX", +0.04523279994605788),
          ("IXZX", -0.04523279994605788),
          ("ZXZX", -0.04523279994605788),
      ]
  )
  
  algorithm_globals.random_seed = 42
  calc = AdaptVQE(VQE(Estimator(), ansatz, self.optimizer))
  res = calc.compute_minimum_eigenvalue(operator=h2_op)
  
  print(calc.eigenvalue_history)
  

  the returned value of calc.history should be roughly [-1.85727503] as there is a single iteration.

• The runtime logging when running the AdaptVQE has been improved. When running the class now, DEBUG and INFO level log messages will be emitted as the class runs.

• Added a new transpiler pass, CollectAndCollapse, to collect and to consolidate blocks of nodes in a circuit. This pass is designed to be a general base class for combined block collection and consolidation. To be completely general, the work of collecting and collapsing the blocks is done via functions provided during instantiating the pass. For example, the CollectLinearFunctions has been updated to inherit from CollectAndCollapse and collects blocks of CXGate and SwapGate gates, and replaces each block with a LinearFunction. The CollectCliffords which is also now based on CollectAndCollapse, collects blocks of « Clifford » gates and replaces each block with a Clifford.

  The interface also supports the option do_commutative_analysis, which allows to exploit commutativity between gates in order to collect larger blocks of nodes. For example, collecting blocks of CX gates in the following circuit:

  qc = QuantumCircuit(2)
  qc.cx(0, 1)
  qc.z(0)
  qc.cx(1, 0)
  

  using do_commutative_analysis enables consolidating the two CX gates, as the first CX gate and the Z gate commute.

• Added a new class BlockCollector that implements various collection strategies, and a new class BlockCollapser that implements various collapsing strategies. Currently BlockCollector includes the strategy to greedily collect all gates adhering to a given filter function (for example, collecting all Clifford gates), and BlockCollapser includes the strategy to consolidate all gates in a block to a single object (or example, a block of Clifford gates can be consolidated to a single Clifford).

• Added a new CollectCliffords transpiler pass that collects blocks of Clifford gates and consolidates these blocks into qiskit.quantum_info.Clifford objects. This pass inherits from CollectAndCollapse and in particular supports the option do_commutative_analysis. It also supports two additional options split_blocks and min_block_size. See the release notes for CollectAndCollapse and CollectLinearFunctions for additional details.

• The CollectLinearFunctions transpiler pass has several new arguments on its constructor:

  • do_commutative_analysis: enables exploiting commutativity between gates in order to collect larger blocks of nodes.

  • split_blocks: enables spliting collected blocks into sub-blocks over disjoint subsets of qubits. For example, in the following circuit:

    qc = QuantumCircuit(4)
    qc.cx(0, 2)
    qc.cx(1, 3)
    qc.cx(2, 0)
    qc.cx(3, 1)
    qc.cx(1, 3)
    

    the single block of CX gates over qubits {0, 1, 2, 3} can be split into two disjoint sub-blocks, one over qubits {0, 2} and the other over qubits {1, 3}.

  • min_block_size: allows to specify the minimum size of the block to be consolidated, blocks with fewer gates will not be modified. For example, in the following circuit:

    qc = QuantumCircuit(4)
    qc.cx(1, 2)
    qc.cx(2, 1)
    

    the two CX gates will be consolidated when min_block_size is 1 or 2, and will remain unchanged when min_block_size is 3 or larger.

• Added a depth-efficient synthesis algorithm synth_cnot_depth_line_kms() for linear reversible circuits LinearFunction over the linear nearest-neighbor architecture, following the paper: https://arxiv.org/abs/quant-ph/0701194.

• The DAGCircuit.replace_block_with_op() method will now return the new DAGOpNode that is created when the block is replaced. Previously, calling this method would not return anything.

• Added a depth-efficient synthesis algorithm synth_permutation_depth_lnn_kms() for Permutation over the linear nearest-neighbor architecture, following the paper: https://arxiv.org/abs/quant-ph/0701194

• Added a new class PermutationGate for representing permutation logic as a circuit element. Unlike the existing Permutation circuit library element which had a static definition this new class avoids synthesizing a permutation circuit when it is declared. This delays the actual synthesis to the transpiler. It also allows enables using several different algorithms for synthesizing permutations, which are available as high-level-synthesis permutation plugins.

  Another key feature of the PermutationGate is that implements the __array__ interface for efficiently returning a unitary matrix for a permutation.

• Added several high-level-synthesis plugins for synthesizing permutations:

  • BasicSynthesisPermutation: applies to fully-connected architectures and is based on sorting. This is the previously used algorithm for constructing quantum circuits for permutations.

  • ACGSynthesisPermutation: applies to fully-connected architectures but is based on the Alon, Chung, Graham method. It synthesizes any permutation in depth 2 (measured in terms of SWAPs).

  • KMSSynthesisPermutation: applies to linear nearest-neighbor architectures and corresponds to the recently added Kutin, Moulton, Smithline method.

  For example:

  from qiskit.circuit import QuantumCircuit
  from qiskit.circuit.library import PermutationGate
  from qiskit.transpiler import PassManager
  from qiskit.transpiler.passes.synthesis.high_level_synthesis import HLSConfig, HighLevelSynthesis
  from qiskit.transpiler.passes.synthesis.plugin import HighLevelSynthesisPluginManager
  
  # Create a permutation and add it to a quantum circuit
  perm = PermutationGate([4, 6, 3, 7, 1, 2, 0, 5])
  qc = QuantumCircuit(8)
  qc.append(perm, range(8))
  
  # Print available plugin names for synthesizing permutations
  # Returns ['acg', 'basic', 'default', 'kms']
  print(HighLevelSynthesisPluginManager().method_names("permutation"))
  
  # Default plugin for permutations
  # Returns a quantum circuit with size 6 and depth 3
  qct = PassManager(HighLevelSynthesis()).run(qc)
  print(f"Default: {qct.size() = }, {qct.depth() = }")
  
  # KMSSynthesisPermutation plugin for permutations
  # Returns a quantum circuit with size 18 and depth 6
  # but adhering to the linear nearest-neighbor architecture.
  qct = PassManager(HighLevelSynthesis(HLSConfig(permutation=[("kms", {})]))).run(qc)
  print(f"kms: {qct.size() = }, {qct.depth() = }")
  
  # BasicSynthesisPermutation plugin for permutations
  # Returns a quantum circuit with size 6 and depth 3
  qct = PassManager(HighLevelSynthesis(HLSConfig(permutation=[("basic", {})]))).run(qc)
  print(f"basic: {qct.size() = }, {qct.depth() = }")
  
  # ACGSynthesisPermutation plugin for permutations
  # Returns a quantum circuit with size 6 and depth 2
  qct = PassManager(HighLevelSynthesis(HLSConfig(permutation=[("acg", {})]))).run(qc)
  print(f"acg: {qct.size() = }, {qct.depth() = }")
  

• Added new classes for Quantum Fisher Information (QFI) and Quantum Geometric Tensor (QGT) algorithms using primitives, qiskit.algorithms.gradients.QFI and qiskit.algorithms.gradients.LinCombQGT, to the gradients module: qiskit.algorithms.gradients. For example:

  from qiskit.circuit import QuantumCircuit, Parameter
  from qiskit.algorithms.gradients import LinCombQGT, QFI
  
  estimator = Estimator()
  a, b = Parameter("a"), Parameter("b")
  qc = QuantumCircuit(1)
  qc.h(0)
  qc.rz(a, 0)
  qc.rx(b, 0)
  
  parameter_value = [[np.pi / 4, 0]]
  
  qgt = LinCombQGT(estimator)
  qgt_result = qgt.run([qc], parameter_value).result()
  
  qfi = QFI(qgt)
  qfi_result = qfi.run([qc], parameter_value).result()
  

• Added a new keyword argument, derivative_type, to the constructor for the LinCombEstimatorGradient. This argument takes a DerivativeType enum that enables specifying to compute only the real or imaginary parts of the gradient.

• Added a new option circuit_reverse_bits to the user config file. This allows users to set a boolean for their preferred default behavior of the reverse_bits argument of the circuit drawers QuantumCircuit.draw() and circuit_drawer(). For example, adding a section to the user config file in the default location ~/.qiskit/settings.conf with:

  [default]
  circuit_reverse_bits = True
  

  will change the default to display the bits in reverse order.

• Added a new class Z2Symmetries to qiskit.quantum_info which is used to identify any \(Z_2\) symmetries from an input SparsePauliOp.

• Added a new pulse directive TimeBlockade. This directive behaves almost identically to the delay instruction, but will be removed before execution. This directive is intended to be used internally within the pulse builder and helps ScheduleBlock represent instructions with absolute time intervals. This allows the pulse builder to convert Schedule into ScheduleBlock, rather than wrapping with Call instructions.

• Added primitive-enabled algorithms for Variational Quantum Time Evolution that implement the interface for Quantum Time Evolution. The qiskit.algorithms.VarQRTE class is used for real and the qiskit.algorithms.VarQITE class is used for imaginary quantum time evolution according to a variational principle passed.

  Each algorithm accepts a variational principle which implements the ImaginaryVariationalPrinciple abstract interface. The following implementations are included:

  • ImaginaryMcLachlanPrinciple

  • RealMcLachlanPrinciple

  For example:

  from qiskit.algorithms import TimeEvolutionProblem, VarQITE
  from qiskit.algorithms.time_evolvers.variational import ImaginaryMcLachlanPrinciple
  from qiskit.circuit.library import EfficientSU2
  from qiskit.quantum_info import SparsePauliOp
  import numpy as np
  
  observable = SparsePauliOp.from_list(
      [
          ("II", 0.2252),
          ("ZZ", 0.5716),
          ("IZ", 0.3435),
          ("ZI", -0.4347),
          ("YY", 0.091),
          ("XX", 0.091),
      ]
  )
  
  ansatz = EfficientSU2(observable.num_qubits, reps=1)
  init_param_values = np.zeros(len(ansatz.parameters))
  for i in range(len(ansatz.parameters)):
      init_param_values[i] = np.pi / 2
  var_principle = ImaginaryMcLachlanPrinciple()
  time = 1
  evolution_problem = TimeEvolutionProblem(observable, time)
  var_qite = VarQITE(ansatz, var_principle, init_param_values)
  evolution_result = var_qite.evolve(evolution_problem)
  

• Added rules for converting XXPlusYYGate and XXMinusYYGate to other gates to the SessionEquivalenceLibrary. This enables running transpile() targeting a backend or Target that uses these gates.

• Added two new fake backends, FakePrague and FakeSherbrooke to the qiskit.providers.fake_provider module. FakePrague provides a backend with a snapshot of the properties from the IBM Prague Egret R1 backend and FakeSherbrooke provides a backend with a snapshot of the properties from the IBM Sherbrooke Eagle R3 backend.

• Added a new keyword argument, allow_unknown_parameters, to the ParameterExpression.bind() and ParameterExpression.subs() methods. When set this new argument enables passing a dictionary containing unknown parameters to these methods without causing an error to be raised. Previously, this would always raise an error without any way to disable that behavior.

• The BaseEstimator.run() method’s observables argument now accepts a str or sequence of str input type in addition to the other types already accepted. When used the input string format should match the Pauli string representation accepted by the constructor for Pauli objects.

• Added a new constructor method QuantumCircuit.from_instructions() that enables creating a QuantumCircuit object from an iterable of instructions. For example:

  from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
  from qiskit.circuit.quantumcircuitdata import CircuitInstruction
  from qiskit.circuit import Measure
  from qiskit.circuit.library import HGate, CXGate
  
  
  qr = QuantumRegister(2)
  cr = ClassicalRegister(2)
  instructions = [
      CircuitInstruction(HGate(), [qr[0]], []),
      CircuitInstruction(CXGate(), [qr[0], qr[1]], []),
      CircuitInstruction(Measure(), [qr[0]], [cr[0]]),
      CircuitInstruction(Measure(), [qr[1]], [cr[1]]),
  ]
  circuit = QuantumCircuit.from_instructions(instructions)
  circuit.draw("mpl")
  

  (Source code)

  

• The Clifford class now takes an optional copy keyword argument in its constructor. If set to False, then a StabilizerTable provided as input will not be copied, but will be used directly. This can have performance benefits, if the data in the table will never be mutated by any other means.

• The performance of Clifford.compose() has been greatly improved for all numbers of qubits. For operators of 20 qubits, the speedup is on the order of 100 times.

• Added a new synthesis function synth_clifford_layers(), for synthesizing a Clifford into layers. The algorithm is based on S. Bravyi, D. Maslov, Hadamard-free circuits expose the structure of the Clifford group, arxiv:2003.09412. This decomposes the Clifford into 8 layers of gates including two layers of CZ gates, and one layer of CX gates. For example, a 5-qubit Clifford circuit is decomposed into the following layers:

       ┌─────┐┌─────┐┌────────┐┌─────┐┌─────┐┌─────┐┌─────┐┌────────┐
  q_0: ┤0    ├┤0    ├┤0       ├┤0    ├┤0    ├┤0    ├┤0    ├┤0       ├
       │     ││     ││        ││     ││     ││     ││     ││        │
  q_1: ┤1    ├┤1    ├┤1       ├┤1    ├┤1    ├┤1    ├┤1    ├┤1       ├
       │     ││     ││        ││     ││     ││     ││     ││        │
  q_2: ┤2 S2 ├┤2 CZ ├┤2 CX_dg ├┤2 H2 ├┤2 S1 ├┤2 CZ ├┤2 H1 ├┤2 Pauli ├
       │     ││     ││        ││     ││     ││     ││     ││        │
  q_3: ┤3    ├┤3    ├┤3       ├┤3    ├┤3    ├┤3    ├┤3    ├┤3       ├
       │     ││     ││        ││     ││     ││     ││     ││        │
  q_4: ┤4    ├┤4    ├┤4       ├┤4    ├┤4    ├┤4    ├┤4    ├┤4       ├
       └─────┘└─────┘└────────┘└─────┘└─────┘└─────┘└─────┘└────────┘
  

  This method will allow to decompose a Clifford in 2-qubit depth \(7n+2\) for linear nearest neighbor (LNN) connectivity.

• The return types for the power() methods on several standard library gate classes have been updated to return more specific gate objects that result in a less lossy and more efficient output. For example, running power() now returns an IGate instance instead of UnitaryGate as was done previously.

  The full list of output types that have been improved are:

  Table 17 Output for power()

  Gate Class	Output Class from power()
  CPhaseGate	CPhaseGate
  CSGate	CPhaseGate
  CSdgGate	CPhaseGate
  IGate	IGate.
  PhaseGate	PhaseGate
  RGate	RGate
  RXGate	RXGate
  RXXGate	RXXGate
  RYGate	RYGate
  RYYGate	RYYGate
  RZGate	RZGate
  RZXGate	RZXGate
  RZZGate	RZZGate
  SdgGate	PhaseGate
  SGate	PhaseGate
  TdgGate	PhaseGate
  TGate	PhaseGate
  XXMinusYYGate	XXMinusYYGate
  XXPlusYYGate	XXPlusYYGate
  ZGate	PhaseGate
  iSwapGate	XXPlusYYGate

• The EquivalenceLibrary is now represented internally as a PyDiGraph, this underlying graph object can be accesed from the new graph attribute. This attribute is intended for use internally in Qiskit and therefore should always be copied before being modified by the user to prevent possible corruption of the internal equivalence graph.

• The Operator.from_circuit() constructor method now will reverse the output permutation caused by the routing/swap mapping stage of the transpiler. By default if a transpiled circuit had Swap gates inserted the output matrix will have that permutation reversed so the returned matrix will be equivalent to the original un-transpiled circuit. If you’d like to disable this default behavior the ignore_set_layout keyword argument can be set to True to do this (in addition to previous behavior of ignoring the initial layout from transpilation). If you’d like to manually set a final layout you can use the new final_layout keyword argument to pass in a Layout object to use for the output permutation.

• Added support to the GateDirection transpiler pass to handle the the symmetric RXXGate, RYYGate, and RZZGate gates. The pass will now correctly handle these gates and simply reverse the qargs order in place without any other modifications.

• Added support for using the Python exponentiation operator, **, with Gate objects is now supported. It is equivalent to running the Gate.power() method on the object.

  For example:

  from qiskit.circuit.library import XGate
  
  sx = XGate() ** 0.5
  

• Added new GaussianSquareDrag pulse shape to the qiskit.pulse.library module. This pulse shape is similar to GaussianSquare but uses the Drag shape during its rise and fall. The correction from the DRAG pulse shape can suppress part of the frequency spectrum of the rise and fall of the pulse which can help avoid exciting spectator qubits when they are close in frequency to the drive frequency of the pulse.

• Added a new keyword argument, method, to the constructors for the FiniteDiffEstimatorGradient and FiniteDiffSamplerGradient classes. The method argument accepts a string to indicate the computation method to use for the gradient. There are three methods, available "central", "forward", and "backward". The definition of the methods are:

  Method	Computation
  "central"	\(\frac{f(x+e)-f(x-e)}{2e}\)
  "forward"	\(\frac{f(x+e) - f(x)}{e}\)
  "backward"	\(\frac{f(x)-f(x-e)}{e}\)

  where \(e\) is the offset epsilon.

• All gradient classes in qiskit.algorithms.gradients now preserve unparameterized operations instead of attempting to unroll them. This allows to evaluate gradients on custom, opaque gates that individual primitives can handle and keeps a higher level of abstraction for optimized synthesis and compilation after the gradient circuits have been constructed.

• Added a TranslateParameterizedGates pass to map only parameterized gates in a circuit to a specified basis, but leave unparameterized gates untouched. The pass first attempts unrolling and finally translates if a parameterized gate cannot be further unrolled.

• The CollectCliffords transpiler pass has been expanded to collect and combine blocks of « clifford gates » into Clifford objects, where « clifford gates » may now also include objects of type LinearFunction, Clifford, and PauliGate. For example:

  from qiskit.circuit import QuantumCircuit
  from qiskit.circuit.library import LinearFunction, PauliGate
  from qiskit.quantum_info.operators import Clifford
  from qiskit.transpiler.passes import CollectCliffords
  from qiskit.transpiler import PassManager
  
  # Create a Clifford
  cliff_circuit = QuantumCircuit(2)
  cliff_circuit.cx(0, 1)
  cliff_circuit.h(0)
  cliff = Clifford(cliff_circuit)
  
  # Create a linear function
  lf = LinearFunction([[0, 1], [1, 0]])
  
  # Create a pauli gate
  pauli_gate = PauliGate("XYZ")
  
  # Create a quantum circuit with the above and also simple clifford gates.
  qc = QuantumCircuit(4)
  qc.cz(0, 1)
  qc.append(cliff, [0, 1])
  qc.h(0)
  qc.append(lf, [0, 2])
  qc.append(pauli_gate, [0, 2, 1])
  qc.x(2)
  
  # Run CollectCliffords transpiler pass
  qct = PassManager(CollectCliffords()).run(qc)
  

  All the gates will be collected and combined into a single Clifford. Thus the final circuit consists of a single Clifford object.

• CouplingMap is now implicitly iterable, with the iteration being identical to iterating through the output of CouplingMap.get_edges(). In other words,

  from qiskit.transpiler import CouplingMap
  coupling = CouplingMap.from_line(3)
  list(coupling) == list(coupling.get_edges())
  

  will now function as expected, as will other iterations. This is purely a syntactic convenience.

• Added a new function synth_cnot_count_full_pmh() which is used to synthesize linear reversible circuits for all-to-all architectures using the Patel, Markov and Hayes method. This function is identical to the available qiskit.transpiler.synthesis.cnot_synth() function but has a more descriptive name and is more logically placed in the package tree. This new function supersedes the legacy function which will likely be deprecated in a future release.

• InstructionScheduleMap has been updated to store backend calibration data in the format of PulseQobj JSON and invokes conversion when the data is accessed for the first time, i.e. lazy conversion. This internal logic update drastically improves the performance of loading backend especially with many calibration entries.

• New module qiskit.pulse.calibration_entries has been added. This contains several wrapper classes for different pulse schedule representations.

  • ScheduleDef

  • CallableDef

  • PulseQobjDef

  These classes implement the get_schedule() and get_signature() methods that returns pulse schedule and parameter names to assign, respectively. These classes are internally managed by the InstructionScheduleMap or backend Target, and thus they will not appear in a typical user programs.

• Introduced a new subclass ScalableSymbolicPulse, as a subclass of SymbolicPulse. The new subclass behaves the same as SymbolicPulse, except that it assumes that the envelope of the pulse includes a complex amplitude pre-factor of the form \(\text{amp} * e^{i \times \text{angle}}\). This envelope shape matches many common pulses, including all of the pulses in the Qiskit Pulse library (which were also converted to amp, angle representation in this release).

  The new subclass removes the non-unique nature of the amp, angle representation, and correctly compares pulses according to their complex amplitude.

• Added a new keyword argument, dtype, to the PauliSumOp.from_list() method. When specified this argument can be used to specify the dtype of the numpy array allocated for the SparsePauliOp used internally by the constructed PauliSumOp.

• Support for importing OpenQASM 3 programs into Qiskit has been added. This can most easily be accessed using the functions qasm3.loads() and qasm3.load(), to load a program directly from a string and indirectly from a filename, respectively. For example, one can now do:

  from qiskit import qasm3
  
  circuit = qasm3.loads("""
    OPENQASM 3.0;
    include "stdgates.inc";
  
    qubit q;
    qubit[5] qr;
    bit c;
    bit[5] cr;
  
    h q;
    c = measure q;
  
    if (c) {
      h qr[0];
      cx qr[0], qr[1];
      cx qr[0], qr[2];
      cx qr[0], qr[3];
      cx qr[0], qr[4];
    } else {
      h qr[4];
      cx qr[4], qr[3];
      cx qr[4], qr[2];
      cx qr[4], qr[1];
      cx qr[4], qr[0];
    }
    cr = measure qr;
  """)
  

  This will load the program into a QuantumCircuit instance in the variable circuit.

  Not all OpenQASM 3 features are supported at first, because Qiskit does not yet have a way to represent advanced classical data processing. The capabilities of the importer will increase along with the capabilities of the rest of Qiskit. The initial feature set of the importer is approximately the same set of features that would be output by the exporter (qasm3.dump() and qasm3.dumps()).

  Note that Qiskit’s support of OpenQASM 3 is not meant to provide a totally lossless representation of QuantumCircuits. For that, consider using qiskit.qpy.

• The primitives-based gradient classes defined by the BaseEstimatorGradient and BaseSamplerGradient abstract classes have been updated to simplify extending the base interface. There are three new internal overridable methods, _preprocess(), _postprocess(), and _run_unique(). _preprocess() enables a subclass to customize the input gradient circuits and parameters, _postprocess enables to customize the output result, and _run_unique enables calculating the gradient of a circuit with unique parameters.

• The SabreLayout transpiler pass has greatly improved performance as it has been re-written in Rust. As part of this rewrite the pass has been transformed from an analysis pass to a transformation pass that will run both layout and routing. This was done to not only improve the runtime performance but also improve the quality of the results. The previous functionality of the pass as an analysis pass can be retained by manually setting the routing_pass argument or using the new skip_routing argument.

• The SabreLayout transpiler pass has a new constructor argument layout_trials. This argument is used to control how many random number generator seeds will be attempted to run SabreLayout with. When set the SABRE layout algorithm is run layout_trials number of times and the best quality output (measured in the lowest number of swap gates added) is selected. These seed trials are executed in parallel using multithreading to minimize the potential performance overhead of running layout multiple times. By default if this is not specified the SabreLayout pass will default to using the number of physical CPUs are available on the local system.

• Added two new classes SciPyRealEvolver and SciPyImaginaryEvolver that implement integration methods for time evolution of a quantum state. The value and standard deviation of observables as well as the times they are evaluated at can be queried as TimeEvolutionResult.observables and TimeEvolutionResult.times. For example:

  from qiskit.algorithms.time_evolvers.time_evolution_problem import TimeEvolutionProblem
  from qiskit.quantum_info import SparsePauliOp
  from qiskit.quantum_info.states.statevector import Statevector
  from qiskit.algorithms import SciPyImaginaryEvolver
  
  initial_state = Statevector.from_label("+++++")
  hamiltonian = SparsePauliOp("ZZZZZ")
  evolution_problem = TimeEvolutionProblem(hamiltonian, 100, initial_state, {"Energy":hamiltonian})
  classic_evolver = SciPyImaginaryEvolver(num_timesteps=300)
  result = classic_evolver.evolve(evolution_problem)
  print(result.observables)
  

• Added the SolovayKitaev transpiler pass to run the Solovay-Kitaev algorithm for approximating single-qubit unitaries using a discrete gate set. In combination with the basis translator, this allows to convert any unitary circuit to a universal discrete gate set, which could be implemented fault-tolerantly.

  This pass can e.g. be used after compiling to U and CX gates:

  from qiskit import transpile
  from qiskit.circuit.library import QFT
  from qiskit.transpiler.passes.synthesis import SolovayKitaev
  
  qft = QFT(3)
  
  # optimize to general 1-qubit unitaries and CX
  transpiled = transpile(qft, basis_gates=["u", "cx"], optimization_level=1)
  
  skd = SolovayKitaev()  # uses T Tdg and H as default basis
  discretized = skd(transpiled)
  
  print(discretized.count_ops())
  

  The decomposition can also be used with the unitary synthesis plugin, as the « sk » method on the UnitarySynthesis transpiler pass:

  from qiskit import QuantumCircuit
  from qiskit.quantum_info import Operator
  from qiskit.transpiler.passes import UnitarySynthesis
  
  circuit = QuantumCircuit(1)
  circuit.rx(0.8, 0)
  unitary = Operator(circuit).data
  
  unitary_circ = QuantumCircuit(1)
  unitary_circ.unitary(unitary, [0])
  
  synth = UnitarySynthesis(basis_gates=["h", "s"], method="sk")
  out = synth(unitary_circ)
  
  out.draw('mpl')
  

  (Source code)

  

• Random-circuit generation with qiskit.circuit.random.random_circuit() is now significantly faster for large circuits.

• Random-circuit generation with qiskit.circuit.random.random_circuit() will now output all « standard » gates in Qiskit’s circuit library (qiskit.circuit.library). This includes two 4-qubit gates C3SXGate and RC3XGate, and the allowed values of max_operands have been expanded accordingly.

• The Optimize1qGatesDecomposition transpiler pass has a new keyword argument, target, on its constructor. This argument can be used to specify a Target object that represnts the compilation target. If used it superscedes the basis argument to determine if an instruction in the circuit is present on the target backend.

• The UnrollCustomDefinitions transpiler pass has a new keyword argument, target, on its constructor. This argument can be used to specify a Target object that represnts the compilation target. If used it superscedes the basis_gates argument to determine if an instruction in the circuit is present on the target backend.

• Added the ReverseEstimatorGradient class for a classical, fast evaluation of expectation value gradients based on backpropagation or reverse-mode gradients. This class uses statevectors and thus provides exact gradients but scales exponentially in system size. It is designed for fast reference calculation of smaller system sizes. It can for example be used as:

  from qiskit.circuit.library import EfficientSU2
  from qiskit.quantum_info import SparsePauliOp
  from qiskit.algorithms.gradients import ReverseEstimatorGradient
  
  observable = SparsePauliOp.from_sparse_list([("ZZ", [0, 1], 1)], num_qubits=10)
  circuit = EfficientSU2(num_qubits=10)
  values = [i / 100 for i in range(circuit.num_parameters)]
  gradient = ReverseEstimatorGradient()
  
  result = gradient.run([circuit], [observable], [values]).result()
  

• Added a new keyword argument, use_dag to the constructor for the OneQubitEulerDecomposer class. When use_dag is set to True the output from the decomposer will be a DAGCircuit object instead of QuantumCircuit object. This is useful for transpiler passes that use OneQubitEulerDecomposer (such as Optimize1qGatesDecomposition) as working directly with a DAGCircuit avoids the overhead of converting between QuantumCircuit and DAGCircuit.

• Added the ability for analysis passes to set custom heuristic weights for the VF2Layout and VF2PostLayout transpiler passes. If an analysis pass sets the vf2_avg_error_map key in the property set, its value is used for the error weights instead of the error rates from the backend’s Target (or BackendProperties for BackendV1). The value should be an ErrorMap instance, where each value represents the avg error rate for all 1 or 2 qubit operation on those qubits. If a value is NaN, the corresponding edge is treated as an ideal edge (or qubit for 1q operations). For example, an error map created as:

  from qiskit.transpiler.passes.layout.vf2_utils import ErrorMap
  
  error_map = ErrorMap(3)
  error_map.add_error((0, 0), 0.0024)
  error_map.add_error((0, 1), 0.01)
  error_map.add_error((1, 1), 0.0032)
  

  describes a 2 qubit target, where the avg 1q error rate is 0.0024 on qubit 0 and 0.0032 on qubit 1, the avg 2q error rate for gates that operate on (0, 1) is 0.01, and (1, 0) is not supported by the target. This will be used for scoring if it’s set for the vf2_avg_error_map key in the property set when VF2Layout and VF2PostLayout are run. For example:

  from qiskit.transpiler import AnalysisPass, PassManager, Target
  from qiskit.transpiler.passes import VF2Layout
  from qiskit.transpiler.passes.layout.vf2_utils import ErrorMap
  from qiskit.circuit.library import CZGate, UGate
  from qiskit.circuit import Parameter
  
  class CustomVF2Scoring(AnalysisPass):
    """Set custom score for vf2."""
  
    def run(self, dag):
      error_map = ErrorMap(3)
      error_map.add_error((0, 0), 0.0024)
      error_map.add_error((0, 1), 0.01)
      error_map.add_error((1, 1), 0.0032)
      self.property_set["vf2_avg_error_map"] = error_map
  
  
  target = Target(num_qubits=2)
  target.add_instruction(
      UGate(Parameter('theta'), Parameter('phi'), Parameter('lam')),
      {(0,): None, (1,): None}
  )
  target.add_instruction(
      CZGate(), {(0, 1): None}
  )
  
  vf2_pass = VF2Layout(target=target, seed=1234568942)
  pm = PassManager([CustomVF2Scoring(), vf2_pass])
  

  That will run VF2Layout with the custom scoring from error_map for a 2 qubit Target that doesn’t contain any error rates.

Upgrade Notes

• When initializing any of the pulse classes in qiskit.pulse.library:

  • Gaussian

  • GaussianSquare

  • Drag

  • Constant

  providing a complex amp argument with a finite angle will result in PulseError now. For example, instead of calling Gaussian(duration=100,sigma=20,amp=0.5j) one should use Gaussian(duration=100,sigma=20,amp=0.5,angle=np.pi/2) instead now. The pulse envelope which used to be defined as amp * ... is in turn defined as amp * exp(1j * angle) * .... This change was made to better support Qiskit Experiments where the amplitude and angle of pulses are calibrated in separate experiments.

• For Python 3.7 singledispatchmethod is now a dependency. This was added to enable leveraging the method dispatch mechanism in the standard library of newer versions of Python. If you’re on Python >= 3.8 there is no extra dependency required.

• The previously deprecated MSBasisDecomposer transpiler pass available via the qiskit.transpiler.passes module has been removed. It was originally deprecated as part of the Qiskit Terra 0.16.0 release (10-16-2020). Instead the BasisTranslator transpiler pass should be used instead to translate a circuit into an appropriate basis with a RXXGate

• EquivalenceLibrary objects that are initialized with the base attribute will no long have a shared reference with the EquivalenceLibrary passed in. In earlier releases if you mutated base after it was used to create a new EquivalenceLibrary instance both instances would reflect that change. This no longer is the case and updates to base will no longer be reflected in the new EquivalenceLibrary. For example, if you created an equivalence library with:

  import math
  
  from qiskit.circuit import QuantumCircuit
  from qiskit.circuit.library import XGate
  from qiskit.circuit.equivalence import EquivalenceLibrary
  
  original_lib = EquivalenceLibrary()
  qc = QuantumCircuit(1)
  qc.rx(math.pi, 0)
  original_lib.add_equivalence(XGate(), qc)
  new_lib = EquivalenceLibrary(base=original_lib)
  

  if you modified original_lib with:

  import from qiskit.circuit.library import SXGate
  
  qc = QuantumCircuit(1)
  qc.rx(math.pi / 2, 0)
  original_lib.add_equivalence(SXGate(), qc)
  

  in previous releases new_lib would also include the definition of SXGate after it was added to original_lib, but in this release this no longer will be the case. This change was made because of the change in internal data structure to be a graph, which improved performance of the EquivalenceLibrary class, especially when there are multiple runs of the BasisTranslator transpiler pass.

• The initial_state argument for the constructor of the NLocal class along with assigning directly to the NLocal.initial_state atrribute must be a QuantumCircuit now. Support for using other types for this argument and attribute is no longer supported. Support for other types was deprecated as part of the Qiskit Terra 0.18.0 release (July 2021).

• The LaTeX array drawers (e.g. array_to_latex, Statevector.draw('latex')) now use the same sympy function as the ket-convention drawer. This means it may render some numbers differently to previous releases, but will provide a more consistent experience. For example, it may identify new factors, or rationalize denominators where it did not previously. The default precision has been changed from 5 to 10.

• The QPY version format version emitted by dump() has been increased to version 6. This new format version is incompatible with the previous versions and will result in an error when trying to load it with a deserializer that isn’t able to handle QPY version 6. This change was necessary to support the introduction of ScalableSymbolicPulse which was handled by adding a class_name_size attribute to the header of the dumped SymbolicPulse objects.

• The __hash__ method for the SymbolicPulse was removed. This was done to reflect the mutable nature (via parameter assignment) of this class which could result in errors when using SymbolicPulse in situtations where a hashable object was required. This means the builtin hash() method and using SymbolicPulse as keys in dictionaries or set members will no longer work.

• The names of Register instances (which includes instances of QuantumRegister and ClassicalRegigster) are no longer constrained to be valid OpenQASM 2 identifiers. This is being done as the restriction is overly strict as Qiskit becomes more decoupled from OpenQASM 2, and even the OpenQASM 3 specification is not so restrictive. If you were relying on registers having valid OpenQASM 2 identifier names, you will need to begin escaping the names. A simplistic version of this could be done, for example, by:

  import re
  import string
  
  def escape(name: str) -> str:
    out = re.sub(r"\W", "_", name, flags=re.ASCII)
    if not out or out[0] not in string.ascii_lowercase:
      return "reg_" + out
    return out
  

• The QuantumCircuit methods u1, u2, u3, and their controlled variants cu1, cu3 and mcu1 have been removed following their deprecation in Qiskit Terra 0.16.0. This was to remove gate names that were usually IBM-specific, in favour of the more general methods p(), u(), cp() and cu(). The gate classes U1Gate, U2Gate and U3Gate are still available for use with QuantumCircuit.append(), so backends can still support bases with these gates explicitly given.

• The QuantumCircuit methods combine and extend have been removed following their deprecation in Qiskit Terra 0.17.0. This was done because these functions were simply less powerful versions of QuantumCircuit.compose(), which should be used instead.

  The removal of extend also means that the + and += operators are no longer defined for QuantumCircuit. Instead, you can use the & and &= operators respectively, which use QuantumCircuit.compose().

• The previously deprecated functions: qiskit.circuit.measure.measure() and qiskit.circuit.reset.reset() have been removed. These functions were deprecated in the Qiskit Terra 0.19.0 release (December, 2021). Instead you should use the QuantumCircuit.measure() and QuantumCircuit.reset() methods of the QuantumCircuit object you wish to append a Measure or Reset operation to.

• The previously deprecated ParameterView methods which were inherited from set have been removed from ParameterView, the type returned by QuantumCircuit.parameters. The specific methods which have been removed are:

  • add()

  • difference()

  • difference_update()

  • discard()

  • intersection()

  • intersection_update()

  • issubset()

  • issuperset()

  • symmetric_difference()

  • symmetric_difference_update()

  • union()

  • update()

  along with support for the Python operators:

  • ixor: ^=

  • isub: -=

  • ior: |=

  These were deprecated in the Qiskit Terra 0.17.0 release (April, 2021). The ParameterView type is now a general sequence view type and doesn’t support these set operations any longer.

• The previously deprecated NetworkX converter methods for the DAGCircuit and DAGDependency classes: DAGCircuit.to_networkx(), DAGCircuit.from_networkx(), and DAGDependency.to_networkx() have been removed. These methods were originally deprecated as part of the Qiskit Terra 0.21.0 release (June, 2022). Qiskit has been using rustworkx as its graph library since the qiskit-terra 0.12.0 release and since then the NetworkX converter function have been a lossy process. They were originally added so that users could leverage NetworkX’s algorithms library to leverage functionality not present in DAGCircuit and/or rustworkx. However, since that time both DAGCircuit and rustworkx has matured and offers more functionality and the DAGCircuit is tightly coupled to rustworkx for its operation and having these converter methods provided limited functionality and therefore have been removed.

• tweedledum has been removed as a core requirement of Qiskit Terra. The functionality provided (qiskit.circuit.classicalfunction) is still available, if tweedledum is installed manually, such as by:

  pip install tweedledum
  

  This change was made because tweedledum development has slowed to the point of not keeping up with new Python and OS releases, and was blocking some Qiskit users from installing Qiskit.

• The lazy optional checkers HAS_MATPLOTLIB, HAS_PIL, HAS_PYLATEX and HAS_PDFTOCAIRO are no longer exposed from qiskit.visualization, having been deprecated in Qiskit Terra 0.21. The canonical location for these (and many other lazy checkers) is qiskit.utils.optionals, and all four objects can be found there.

• The previously deprecated gate argument to the constructor of the Decompose transpiler pass, along with its matching attribute Decompose.gate have been removed. The argument and attribute were deprecated as part of the Qiskit Terra 0.19.0 release (December, 2021). Instead the gates_to_decompose argument for the constructor along with the Decompose.gates_to_decompose attribute should be used instead. The gates_to_decompose argument and attribute should function the same, but has a more explicit name and also enables specifying lists of gates instead of only supporting a single gate.

• The previously deprecated label argument for the constructor of the MCMT and MCMTVChain classes has been removed. It was deprecated as of the Qiskit Terra 0.19.0 release (Decemeber, 2021). Using the label argument on these classes was undefined behavior as they are subclasses of QuantumCircuit instead of Instruction. This would result in the assigned label generally being ignored. If you need to assign a label to an instance of MCMT or MCMTVChain you should convert them to an Gate instance with to_gate() and then assign the desired label to label attribute. For example:

  from qiskit.circuit.library import MCMT, XGate
  
  mcmt_circuit = MCMT(XGate(), 3, 2)
  mcmt_gate = mcmt_circuit.to_gate()
  mcmt_gate.label = "Custom MCMT X"
  

• The retworkx dependency for Qiskit has been removed and replaced by rustworkx library. These are the same packages, but rustworkx is the new name for retworkx which was renamed as part of their combined 0.12.0 release. If you were previously using retworkx 0.12.0 with Qiskit then you already installed rustworkx (retworkx 0.12.0 was just a redirect shim for backwards compatibility). This change was made to migrate to the new package name which will be the only supported package in the future.

• The default behavior of the SabreLayout compiler pass has changed. The pass is no longer an AnalysisPass and by default will compute the initital layout, apply it to the circuit, and will also run SabreSwap internally and apply the swap mapping and set the final_layout property set with the permutation caused by swap insertions. This means for users running SabreLayout as part of a custom PassManager will need to adjust the pass manager to account for this (unless they were setting the routing_pass argument for SabreLayout). This change was made in the interest of improving the quality output, the layout and routing quality are highly coupled and SabreLayout will now run multiple parallel seed trials and to calculate which seed provides the best results it needs to perform both the layout and routing together. There are three ways you can adjust the usage in your custom pass manager. The first is to avoid using embedding in your preset pass manager. If you were previously running something like:

  from qiskit.transpiler import PassManager
  from qiskit.transpiler.preset_passmanagers import common
  from qiskit.transpiler.passes.SabreLayout
  
  pm = PassManager()
  pm.append(SabreLayout(coupling_map)
  pm += common.generate_embed_passmanager(coupling_map)
  

  to compute the layout and then apply it (which was typically followed by routing) you can adjust the usage to just simply be:

  from qiskit.transpiler import PassManager
  from qiskit.transpiler.preset_passmanagers import common
  from qiskit.transpiler.passes.SabreLayout
  
  pm = PassManager()
  pm.append(SabreLayout(coupling_map)
  

  as SabreLayout will apply the layout and you no longer need the embedding stage. Alternatively, you can specify the routing_pass argument which will revert SabreLayout to its previous behavior. For example, if you want to run SabreLayout as it was run in previous releases you can do something like:

  from qiskit.transpiler.passes import SabreSwap, SabreLayout
  routing_pass = SabreSwap(
      coupling_map, "decay", seed=seed, fake_run=True
  )
  layout_pass = SabreLayout(coupling_map, routing_pass=routing_pass, seed=seed)
  

  which will have SabreLayout run as an analysis pass and just set the layout property set. The final approach is to leverage the skip_routing argument on SabreLayout, when this argument is set to True it will skip applying the found layout and inserting the swap gates from routing. However, doing this has a runtime penalty as SabreLayout will still be computing the routing and just does not use this data. The first two approaches outlined do not have additional overhead associated with them.

• The layouts computed by the SabreLayout pass (when run without the routing_pass argument) with a fixed seed value may change from previous releases. This is caused by a new random number generator being used as part of the rewrite of the SabreLayout pass in Rust which significantly improved the performance. If you rely on having consistent output you can run the pass in an earlier version of Qiskit and leverage qiskit.qpy to save the circuit and then load it using the current version. Alternatively you can explicitly set the routing_pass argument to an instance of SabreSwap to mirror the previous behavior of SabreLayout:

  from qiskit.transpiler.passes import SabreSwap, SabreLayout
  
  
  routing_pass = SabreSwap(
      coupling_map, "decay", seed=seed, fake_run=True
  )
  layout_pass = SabreLayout(coupling_map, routing_pass=routing_pass, seed=seed)
  

  which will mirror the behavior of the pass in the previous release. Note, that if you were using the swap_trials argument on SabreLayout in previous releases when adjusting the usage to this form that you will need to set trials argument on the SabreSwap constructor if you want to retain the previous output with a fixed seed.

• The exact circuit returned by qiskit.circuit.random.random_circuit for a given seed has changed. This is due to efficiency improvements in the internal random-number generation for the function.

• The version requirement for the optional feature package qiskit-toqm, installable via pip install qiskit-terra[toqm], has been upgraded from version 0.0.4 to 0.1.0. To use the toqm routing method with transpile() you must now use qiskit-toqm version 0.1.0 or newer. Older versions are no longer discoverable by the transpiler.

• The output QuasiDistribution from the Sampler.run method has been updated to filter out any states with a probability of zero. Now if a valid state is missing from the dictionary output it can be assumed to have a 0 probability. Previously, all possible outcomes for a given number of bits (e.g. for a 3 bit result 000, 001, 010, 011, 100, 101, 110, and 111) even if the probability of a given state was 0. This change was made to reduce the size of the output as for larger number of bits the output size could be quite large. Also, filtering the zero probability results makes the output consistent with other implementations of BaseSampler.

• The behavior of the pulse builder when a Schedule is called has been upgraded. Called schedules are internally converted into ScheduleBlock representation and now reference mechanism is always applied rather than appending the schedules wrapped by the Call instruction. Note that the converted block doesn’t necessary recover the original alignment context. This is simply an ASAP aligned sequence of pulse instructions with absolute time intervals. This is an upgrade of internal representation of called pulse programs and thus no API changes. However the Call instruction and Schedule no longer appear in the builder’s pulse program. This change guarantees the generated schedule blocks are always QPY compatible. If you are filtering the output schedule instructions by Call, you can access to the ScheduleBlock.references instead to retrieve the called program.

• RZXCalibrationBuilder and RZXCalibrationBuilderNoEcho transpiler pass have been upgraded to generate ScheduleBlock. This change guarantees the transpiled circuits are always QPY compatible. If you are directly using rescale_cr_inst(), method from another program or a pass subclass to rescale cross resonance pulse of the device, now this method is turned into a pulse builder macro, and you need to use this method within the pulse builder context to adopts to new release. The method call injects a play instruction to the context pulse program, instead of returning a Play instruction with the stretched pulse.

Deprecation Notes

• Support for running Qiskit with Python 3.7 support has been deprecated and will be removed in the qiskit-terra 0.25.0 release. This means starting in the 0.25.0 release you will need to upgrade the Python version you’re using to Python 3.8 or above.

• The class LinearFunctionsSynthesis class is now deprecated and will be removed in a future release. It has been superseded by the more general HighLevelSynthesis class which should be used instead. For example, you can instantiate an instance of HighLevelSynthesis that will behave the same way as LinearFunctionSynthesis with:

  from qiskit.transpiler.passes import HighLevelSynthesis
  from qiskit.transpiler.passes.synthesis.high_level_synthesis import HLSConfig
  
  HighLevelSynthesis(
      HLSConfig(
          linear_function=[("default", {})],
          use_default_on_unspecified=False,
      )
  )
  

• Support for passing in lists of argument values to the transpile() function is deprecated and will be removed in the 0.25.0 release. This is being done to facilitate greatly reducing the overhead for parallel execution for transpiling multiple circuits at once. If you’re using this functionality currently you can call transpile() multiple times instead. For example if you were previously doing something like:

  from qiskit.transpiler import CouplingMap
  from qiskit import QuantumCircuit
  from qiskit import transpile
  
  qc = QuantumCircuit(2)
  qc.h(0)
  qc.cx(0, 1)
  qc.measure_all()
  cmaps = [CouplingMap.from_heavy_hex(d) for d in range(3, 15, 2)]
  results = transpile([qc] * 6, coupling_map=cmaps)
  

  instead you should run something like:

  from itertools import cycle
  from qiskit.transpiler import CouplingMap
  from qiskit import QuantumCircuit
  from qiskit import transpile
  
  qc = QuantumCircuit(2)
  qc.h(0)
  qc.cx(0, 1)
  qc.measure_all()
  cmaps = [CouplingMap.from_heavy_hex(d) for d in range(3, 15, 2)]
  
  results = []
  for qc, cmap in zip(cycle([qc]), cmaps):
      results.append(transpile(qc, coupling_map=cmap))
  

  You can also leverage parallel_map() or multiprocessing from the Python standard library if you want to run this in parallel.

• The legacy version of the pulse drawer present in the qiskit.visualization.pulse has been deprecated and will be removed in a future release. This includes the ScheduleDrawer and :class`WaveformDrawer` classes. This module has been superseded by the qiskit.visualization.pulse_v2 drawer and the typical user API pulse_drawer() and PulseBlock.draw() are already updated internally to use qiskit.visualization.pulse_v2.

• The pulse.Instruction.draw() method has been deprecated and will removed in a future release. The need for this method has been superseded by the qiskit.visualization.pulse_v2 drawer which doesn’t require Instrucion objects to have their own draw method. If you need to draw a pulse instruction you should leverage the pulse_drawer() instead.

• The import qiskit.circuit.qpy_serialization is deprecated, as QPY has been promoted to the top level. You should import the same objects from qiskit.qpy instead. The old path will be removed in a future of Qiskit Terra.

• The qiskit.IBMQ object is deprecated. This alias object lazily redirects attribute access to qiskit.providers.ibmq.IBMQ. As the qiskit-ibmq-provider package has been supersceded by qiskit-ibm-provider package which maintains its own namespace maintaining this alias is no longer relevant with the new package. If you were relying on the qiskit.IBMQ alias you should update your usage to use qiskit.providers.ibmq.IBMQ directly instead (and also consider migrating to qiskit-ibm-provider, see the migration guide for more details).

• Several public methods of pulse Qobj converters have been deprecated and in a future release they will no longer be directly callable. The list of methods is:

  In InstructionToQobjConverter,

  • convert_acquire()

  • convert_bundled_acquires()

  • convert_set_frequency()

  • convert_shift_frequency()

  • convert_set_phase()

  • convert_shift_phase()

  • convert_delay()

  • convert_play()

  • convert_snapshot()

  In QobjToInstructionConverter,

  • convert_acquire()

  • convert_set_phase()

  • convert_shift_phase()

  • convert_set_frequency()

  • convert_shift_frequency()

  • convert_delay()

  • bind_pulse()

  • convert_parametric()

  • convert_snapshot()

  Instead of calling any of these methods directly they will be implicitly selected when a converter instance is directly called. For example:

  converter = QobjToInstructionConverter()
  converter(pulse_qobj)
  

• The qiskit.visualization.state_visualization.num_to_latex_ket() and qiskit.visualization.state_visualization.num_to_latex_terms() functions have been deprecated and will be removed in a future release. These function were primarily used internally by the LaTeX output from Statevector.draw() and DensityMatrix.draw() which no longer are using these function and are leverging sympy for this instead. If you were using these functions you should cosinder using Sympy’s nsimplify() latex() functions.

• The method Register.qasm() is deprecated and will be removed in a future release. This method is found on the subclasses QuantumRegister and ClassicalRegister. The deprecation is because the qasm() method promotes a false view of the responsible party for safe conversion to OpenQASM 2; a single object alone does not have the context to provide a safe conversion, such as whether its name clashes after escaping it to produce a valid identifier.

• The class-variable regular expression Register.name_format is deprecated and wil be removed in a future release. The names of registers are now permitted to be any valid Python string, so the regular expression has no use any longer.

• The functions qiskit.quantum_info.synthesis.decompose_clifford() and qiskit.quantum_info.synthesis.decompose_cnot_dihedral() are deprecated and will be removed in a future release. They are replaced by the two functions qiskit.synthesis.synth_clifford_full() and qiskit.synthesis.synth_cnotdihedral_full() respectively.

Bug Fixes

• Fixed an issue in the PauliOp.adjoint() method where it would return the correct value for Paulis with complex coefficients, for example: PauliOp(Pauli("iX")). Fixed #9433.

• Fixed an issue with the amplitude estimation algorithms in the qiskit.algorithms.amplitude_estimators module (see amplitude_estimators) for the usage with primitives built from the abstract BaseSampler primitive (such as Sampler and BackendSampler). Previously, the measurement results were expanded to more bits than actually measured which for oracles with more than one qubit led to potential errors in the detection of the « good » quantum states for oracles.

• Fixed an issue where the QuantumCircuit.add_calibrations() and DAGCircuit.add_calibrations() methods had a mismatch in their behavior of parameter-formatting logic. Previously DAGCircuit.add_calibrations() tried to cast every parameter into float, QuantumCircuit.add_calibrations() used given parameters as-is. This would potentially cause an error when running transpile() on a QuantumCircuit with pulse gates as the parameters of the calibrations could be kept as ParameterExpresion objects.

• Fixed a deserialization issue in QPY’s (qiskit.qpy) load() function where circuits containing gates of class MCXGate, MCXGrayCode, MCXRecursive, and MCXVChain would fail to deserialize. Fixed #9390.

• Fixed an issue in TensoredOp.to_matrix() where the global coefficient of the operator was multiplied to the final matrix more than once. Now, the global coefficient is correctly applied, independent of the number of tensored operators or states. Fixed #9398.

• The output from the run() method of the the BackendSampler class now sets the shots and stddev_upper_bound attributes of the returned QuasiDistribution. Previously these attributes were missing which prevent some post-processing using the output. Fixed #9311

• The OpenQASM 2 exporter method QuantumCircuit.qasm() will now emit higher precision floating point numbers for gate parameters by default. In addition, a tighter bound (\(1e-12\) instead of \(1e-6\)) is used for checking whether a given parameter is close to a fraction/power of \(\pi\). Fixed #7166.

• Fixed support in the primitives module for running QuantumCircuit objects with control flow instructions (e.g. IfElseOp). Previously, the BaseSampler and BaseEstimator base classes could not correctly normalize such circuits. However, executing these circuits is dependent on the particular implementation of the primitive supporting control flow instructions. This just fixed support to enable a particular implementation of BaseSampler or BaseEstimator to use control flow instructions.

• Fixed an issue with the PauliOp.matmul() method where it would return incorrect results with iI. Fixed #8680.

• Fixed an issue with the Approximate Quantum Compiler (AQC) class which caused it to return an incorrect circuit when the input unitary had a determinant of -1. Fixed #9327

• Fixed an issue with the QuantumCircuit.compose() method where it would incorrectly reject valid qubit or clbit specifiers. This has been fixed so that the method now accepts the same set of qubit and clbit specifiers as other QuantumCircuit methods, such as append(). Fixed #8691.

• Fixed an issue with the QuantumCircuit.compose() method where it would incorrectly map registers in conditions on the given circuit to complete registers on the base. Previously, the mapping was very imprecise; the bits used within each condition were not subject to the mapping, and instead an inaccurate attempt was made to find a corresponding register. This could also result in a condition on a smaller register being expanded to be on a larger register, which is not a valid transformation. Now, a condition on a single bit or a register will be composed to be on precisely the bits as defined by the clbits argument. A new aliasing register will be added to the base circuit to facilitate this, if necessary. Fixed #6583.

• Fixed an issue with the transpile() function when run with optimization_level set to 1, 2, or 3 and no backend, basis_gates, or target argument specified. If the input circuit had runs of single qubit gates which could be simplified the output circuit would not be as optimized as possible as those runs of single qubit gates would not have been removed. This could have been corrected previously by specifying either the backend, basis_gates, or target arguments on the transpile() call, but now the output will be as simplified as it can be without knowing the target gates allowed. Fixed #9217

• Fixed an issue with the transpile() function when run with optimization_level=3 and no backend, basis_gates, or target argument specified. If the input circuit contained any 2 qubit blocks which were equivalent to an identity matrix the output circuit would not be as optimized as possible and and would still contain that identity block. This could have been corrected previously by specifying either the backend, basis_gates, or target arguments on the transpile() call, but now the output will be as simplified as it can be without knowing the target gates allowed. Fixed #9217

• Fixed an issue with LinCombSamplerGradient where it would potentially raise an error when run with the Sampler class from qiskit-aer.

• Fixed an issue with NumPyEigensolver and by extension NumPyMinimumEigensolver where solving for BaseOperator subclasses other than Operator would cause an error.

• Fixed an issue in the metadata output from primitives where the list made copies by reference and all elements were updated with the same value at every iteration.

• Fixed an issue with the QobjToInstructionConverter when multiple backends are called and they accidentally have the same pulse name in the pulse library. This was an edge case that could only be caused when a converter instance was reused across multiple backends (this was not a typical usage pattern).

• Fixed an issue with the PVQD class where the loss function was incorrecly squaring the fidelity. This has been fixed so that the loss function matches the definition in the original algorithm definition.

• Fixed a bug in QPY (qiskit.qpy) where circuits containing registers whose bits occurred in the circuit after loose bits would fail to deserialize. See #9094.

• The class TwoQubitWeylDecomposition is now compatible with the pickle protocol. Previously, it would fail to deserialize and would raise a TypeError. See #7312.

• Fixed an issue with the LocalReadoutMitigator.quasi_probabilities() method where the shots argument was not used. It is now used to set the number of shots in the return object.

• Fixed a regression in the construction of Clifford objects from QuantumCircuits that contain other Clifford objects.

• Fixed an issue with the TwoQubitWeylDecomposition class (and its subclasses) to enable the Python standard library pickle to serialize these classes. This partially fixed #7312

• QuantumCircuit.qasm() will now correctly escape gate and register names that collide with reserved OpenQASM 2 keywords. Fixes #5043.

• Fixed an issue in the RZXCalibrationBuilder where the ECR pulse sequence was misaligned. Fixed #9013.

• Fixed an issue with the pulse_drawer() where in some cases the output visualization would omit some of the channels in a schedule. Fixed #8981.

Aer 0.11.2

No change

IBM Q Provider 0.19.2

No change

Qiskit 0.39.5

Terra 0.22.4

Prelude

Qiskit Terra 0.22.4 is a minor bugfix release, fixing some bugs identified in the 0.22 series.

Bug Fixes

• Fixed a bug in BackendSampler that raised an error if its run() method was called two times sequentially.

• Fixed two bugs in the ComposedOp where the ComposedOp.to_matrix() method did not provide the correct results for compositions with StateFn and for compositions with a global coefficient. Fixed #9283.

• Fixed the problem in which primitives, Sampler and Estimator, did not work when passed a circuit with numpy.ndarray as a parameter.

• Fixed a bug in SamplingVQE where the aggregation argument did not have an effect. Now the aggregation function and, with it, the CVaR expectation value can correctly be specified.

• Fixed a performance bug where SamplingVQE evaluated the energies of eigenstates in a slow manner.

• Fixed the autoevaluation of the beta parameters in VQD, added support for SparsePauliOp inputs, and fixed the energy evaluation function to leverage the asynchronous execution of primitives, by only retrieving the job results after both jobs have been submitted.

• Fixed an issue with the Statevector.probabilities_dict() and DensityMatrix.probabilities_dict() methods where they would return incorrect results for non-qubit systems when the qargs argument was specified. Fixed #9210

• Fixed handling of some classmethods by wrap_method() in Python 3.11. Previously, in Python 3.11, wrap_method would wrap the unbound function associated with the classmethod and then fail when invoked because the class object usually bound to the classmethod was not passed to the function. Starting in Python 3.11.1, this issue affected QiskitTestCase, preventing it from being imported by other test code. Fixed #9291.

Aer 0.11.2

No change

IBM Q Provider 0.19.2

No change

Qiskit 0.39.4

Terra 0.22.3

No change

Aer 0.11.2

New Features

• Added support for running Qiskit Aer with Python 3.11 support.

Known Issues

• Fix two bugs in AerStatevector. AerStatevector uses mc* instructions, which are not enabled in matrix_product_state method. This commit changes AerStatevector not to use MC* and use H, X, Y, Z, U and CX. AerStatevector also failed if an instruction is decomposed to empty QuantumCircuit. This commit allows such instruction.

Bug Fixes

• Fixed support in the AerSimulator.from_backend() method for instantiating an AerSimulator instance from an a BackendV2 object. Previously, attempting to use AerSimulator.from_backend() with a BackendV2 object would have raised an AerError saying this wasn’t supported.

• Fixes a bug where NoiseModel.from_backend() with a BackendV2 object may generate a noise model with excessive QuantumError s on non-Gate instructions while, for example, only ReadoutError s should be sufficient for measures. This commit updates NoiseModel.from_backend() with a BackendV2 object so that it returns the same noise model as that called with the corresponding BackendV1 object. That is, the resulting noise model does not contain any QuantumError s on measures and it may contain only thermal relaxation errors on other non-gate instructions such as resets. Note that it still contains ReadoutError s on measures.

• Fixed a bug in NoiseModel.from_backend() where using the temperature kwarg with a non-default value would incorrectly compute the excited state population for the specified temperature. Previously, there was an additional factor of 2 in the Boltzman distribution calculation leading to an incorrect smaller value for the excited state population.

• Fixed incorrect logic in the control-flow compiler that could allow unrelated instructions to appear « inside » control-flow bodies during execution, causing incorrect results. For example, previously:

  from qiskit import QuantumCircuit
  from qiskit_aer import AerSimulator
  
  backend = AerSimulator(method="statevector")
  
  circuit = QuantumCircuit(3, 3)
  circuit.measure(0, 0)
  circuit.measure(1, 1)
  
  with circuit.if_test((0, True)):
      with circuit.if_test((1, False)):
          circuit.x(2)
  
  with circuit.if_test((0, False)):
      with circuit.if_test((1, True)):
          circuit.x(2)
  
  circuit.measure(range(3), range(3))
  print(backend.run(circuit, method=method, shots=100).result())
  

  would print {'010': 100} as the nested control-flow operations would accidentally jump over the first X gate on qubit 2, which should have been executed.

• Fixes a bug where NoiseModel.from_backend() prints verbose warnings when supplying a backend that reports un-physical device parameters such as T2 > 2 * T1 due to statistical errors in their estimation. This commit removes such warnings because they are not actionable for users in the sense that there are no means other than truncating them to the theoretical bounds as done within noise.device module. See Issue 1631 for details of the fixed bug.

• This is fix for GPU statevector simulator. Chunk distribution tried to allocate all free memory on GPU, but this causes memory allocation error. So this fix allocates 80 percent of free memory. Also this fixes size of matrix buffer when noise sampling is applied.

• This is a fix of AerState running with cache blocking. AerState wrongly configured transpiler of Aer for cache blocking, and then its algorithm to swap qubits worked wrongly. This fix corrects AerState to use this transpiler. More specifically, After the transpilation, a swapped qubit map is recoverd to the original map when using AerState. This fix is necessary for AerStatevector to use multiple-GPUs.

• This is fix for AerStatevector. It was not possible to create an AerStatevector instance directly from terra’s Statevector. This fix allows a Statevector as AerStatevector’s input.

• SamplerResult.quasi_dists contain the data about the number of qubits. QuasiDistribution.binary_probabilities() returns bitstrings with correct length.

• Previously seed is not initialized in AerStatevector and then sampled results are always same. With this commit, a seed is initialized for each sampling and sampled results can be vary.

IBM Q Provider 0.19.2

No change

Qiskit 0.39.3

Terra 0.22.3

Prelude

Qiskit Terra 0.22.3 is a minor bugfix release, fixing some further bugs in the 0.22 series.

Bug Fixes

• AdaptVQE now correctly indicates that it supports auxiliary operators.

• The circuit drawers (QuantumCircuit.draw() and circuit_drawer()) will no longer emit a warning about the cregbundle parameter when using the default arguments, if the content of the circuit requires all bits to be drawn individually. This was most likely to appear when trying to draw circuits with new-style control-flow operations.

• Fixed a bug causing QNSPSA to fail when max_evals_grouped was set to a value larger than 1.

• Fixed an issue with the SabreSwap pass which would cause the output of multiple runs of the pass without the seed argument specified to reuse the same random number generator seed between runs instead of using different seeds. This previously caused identical results to be returned between runs even when no seed was specified.

• Fixed an issue with the primitive classes, BackendSampler and BackendEstimator, where instances were not able to be serialized with pickle. In general these classes are not guaranteed to be serializable as BackendV2 and BackendV1 instances are not required to be serializable (and often are not), but the class definitions of BackendSampler and BackendEstimator no longer prevent the use of pickle.

• The pulse.Instruction.draw() method will now succeed, as before. This method is deprecated with no replacement planned, but it should still work for the period of deprecation.

Aer 0.11.1

No change

IBM Q Provider 0.19.2

No change

Qiskit 0.39.2

Terra 0.22.2

Prelude

Qiskit Terra 0.22.2 is a minor bugfix release, and marks the first official support for Python 3.11.

Bug Fixes

• Fixed an issue with the backend primitive classes BackendSampler and BackendEstimator which prevented running with a BackendV1 instance that does not have a max_experiments field set in its BackendConfiguration.

• Fixed a bug in the VF2PostLayout pass when transpiling for backends with a defined Target, where the interaction graph would be built incorrectly. This could result in excessive runtimes due to the graph being far more complex than necessary.

• The Pulse expression parser should no longer periodically hang when called from Jupyter notebooks. This is achieved by avoiding an internal deepycopy of a recursive object that seemed to be particularly difficult for the memoization to evaluate.

Aer 0.11.1

No change

IBM Q Provider 0.19.2

No change

Qiskit 0.39.1

Terra 0.22.1

Prelude

Qiskit Terra 0.22.1 is a bugfix release, addressing some minor issues identified since the 0.22.0 release.

Deprecation Notes

• The pauli_list kwarg of pauli_basis() has been deprecated as pauli_basis() now always returns a PauliList. This argument was removed prematurely from Qiskit Terra 0.22.0 which broke compatibility for users that were leveraging the pauli_list``argument. Now, the argument has been restored but will emit a ``DeprecationWarning when used. If used it has no effect because since Qiskit Terra 0.22.0 a PauliList is always returned.

Bug Fixes

• Fixed the BarrierBeforeFinalMeasurements transpiler pass when there are conditions on loose Clbits immediately before the final measurement layer. Previously, this would fail claiming that the bit was not present in an internal temporary circuit. Fixed #8923

• The equality checkers for QuantumCircuit and DAGCircuit (with objects of the same type) will now correctly handle conditions on single bits. Previously, these would produce false negatives for equality, as the bits would use « exact » equality checks instead of the « semantic » checks the rest of the properties of circuit instructions get.

• Fixed handling of classical bits in StochasticSwap with control flow. Previously, control-flow operations would be expanded to contain all the classical bits in the outer circuit and not contracted again, leading to a mismatch between the numbers of clbits the instruction reported needing and the actual number supplied to it. Fixed #8903

• Fixed handling of globally defined instructions for the Target class. Previously, two methods, operations_for_qargs() and operation_names_for_qargs() would ignore/incorrectly handle any globally defined ideal operations present in the target. For example:

  from qiskit.transpiler import Target
  from qiskit.circuit.library import CXGate
  
  target = Target(num_qubits=5)
  target.add_instruction(CXGate())
  names = target.operation_names_for_qargs((1, 2))
  ops = target.operations_for_qargs((1, 2))
  

  will now return {"cx"} for names and [CXGate()] for ops instead of raising a KeyError or an empty return.

• Fixed an issue in the Target.add_instruction() method where it would previously have accepted an argument with an invalid number of qubits as part of the properties argument. For example:

  from qiskit.transpiler import Target
  from qiskit.circuit.library import CXGate
  
  target = Target()
  target.add_instruction(CXGate(), {(0, 1, 2): None})
  

  This will now correctly raise a TranspilerError instead of causing runtime issues when interacting with the target. Fixed #8914

• Fixed an issue with the plot_state_hinton() visualization function which would result in a misplaced axis that was offset from the actual plot. Fixed #8446 <https://github.com/Qiskit/qiskit-terra/issues/8446>

• Fixed the output of the plot_state_hinton() function so that the state labels are ordered ordered correctly, and the image matches up with the natural matrix ordering. Fixed #8324

• Fixed an issue with the primitive classes, BackendSampler and BackendEstimator when running on backends that have a limited number of circuits in each job. Not all backends support an unlimited batch size (most hardware backends do not) and previously the backend primitive classes would have potentially incorrectly sent more circuits than the backend supported. This has been corrected so that BackendSampler and BackendEstimator will chunk the circuits into multiple jobs if the backend has a limited number of circuits per job.

• Fixed an issue with the BackendEstimator class where previously setting a run option named monitor to a value that evaluated as True would have incorrectly triggered a job monitor that only worked on backends from the qiskit-ibmq-provider package. This has been removed so that you can use a monitor run option if needed without causing any issues.

• Fixed an issue with the Target.build_coupling_map() method where it would incorrectly return None for a Target object with a mix of ideal globally available instructions and instructions that have qubit constraints. Now in such cases the Target.build_coupling_map() will return a coupling map for the constrained instruction (unless it’s a 2 qubit operation which will return None because globally there is no connectivity constraint). Fixed #8971

• Fixed an issue with the Target.qargs attribute where it would incorrectly return None for a Target object that contained any globally available ideal instruction.

• Fixed the premature removal of the pauli_list keyword argument of the pauli_basis() function which broke existing code using the pauli_list=True future compatibility path on upgrade to Qiskit Terra 0.22.0. This keyword argument has been added back to the function and is now deprecated and will be removed in a future release.

• Fixed an issue in QPY serialization (dump()) when a custom ControlledGate subclass that overloaded the _define() method to provide a custom definition for the operation. Previously, this case of operation was not serialized correctly because it wasn’t accounting for using the potentially _define() method to provide a definition. Fixes #8794

• QPY deserialisation will no longer add extra Clbit instances to the circuit if there are both loose Clbits in the circuit and more Qubits than Clbits.

• QPY deserialisation will no longer add registers named q and c if the input circuit contained only loose bits.

• Fixed the SparsePauliOp.dot() method when run on two operators with real coefficients. To fix this, the dtype that SparsePauliOp can take is restricted to np.complex128 and object. Fixed #8992

• Fixed an issue in the circuit_drawer() function and QuantumCircuit.draw() method where the only built-in style for the mpl output that was usable was default. If another built-in style, such as iqx, were used then a warning about the style not being found would be emitted and the drawer would fall back to use the default style. Fixed #8991

• Fixed an issue with the transpile() where it would previously fail with a TypeError if a custom Target object was passed in via the target argument and a list of multiple circuits were specified for the circuits argument.

• Fixed an issue with transpile() when targeting a Target (either directly via the target argument or via a BackendV2 instance from the backend argument) that contained an ideal Measure instruction (one that does not have any properties defined). Previously this would raise an exception trying to parse the target. Fixed #8969

• Fixed an issue with the VF2Layout pass where it would error when running with a Target that had instructions that were missing error rates. This has been corrected so in such cases the lack of an error rate will be treated as an ideal implementation and if no error rates are present it will just select the first matching layout. Fixed #8970

• Fixed an issue with the VF2PostLayout pass where it would error when running with a Target that had instructions that were missing. In such cases the lack of an error rate will be treated as an ideal implementation of the operation.

• Fixed an issue with the VQD class if more than k=2 eigenvalues were computed. Previously this would fail due to an internal type mismatch, but now runs as expected. Fixed #8982

• Fixed a performance bug where the new primitive-based variational algorithms minimum_eigensolvers.VQE, eigensolvers.VQD and SamplingVQE did not batch energy evaluations per default, which resulted in a significant slowdown if a hardware backend was used.

• Zero-operand gates and instructions will now work with circuit_to_gate(), QuantumCircuit.to_gate(), Gate.control(), and the construction of an Operator from a QuantumCircuit containing zero-operand instructions. This edge case is occasionally useful in creating global-phase gates as part of larger compound instructions, though for many uses, QuantumCircuit.global_phase may be more appropriate.

• Fixes issue where Statevector.evolve() and DensityMatrix.evolve() would raise an exeception for nested subsystem evolution for non-qubit subsystems. Fixes issue #8897

• Fixes bug in Statevector.evolve() where subsystem evolution will return the incorrect value in certain cases where there are 2 or more than non-evolved subsystems with different subsystem dimensions. Fixes issue #8899

Aer 0.11.1

Bug Fixes

• Fixed a potential build error when trying to use CMake 3.18 or newer and building qiskit-aer with GPU support enabled. Since CMake 3.18 or later when building with CUDA the CMAKE_CUDA_ARCHITECTURES was required to be set with the architecture value for the target GPU. This has been corrected so that setting AER_CUDA_ARCH will be used if this was not set.

• Fixes a bug in the handling of instructions with clbits in LocalNoisePass. Previously, it was accidentally erasing clbits of instructions (e.g. measures) to which the noise is applied in the case of method="append".

• Fixed the performance overhead of the Sampler class when running with identical circuits on multiple executions. This was accomplished by skipping/caching the transpilation of these identical circuits on subsequent executions.

• Fixed compatibility of the Sampler and Estimator primitive classes with qiskit-terra 0.22.0 release. In qiskit-terra 0.22.0 breaking API changes were made to the abstract interface which broke compatibility with these classes, this has been addressed so that Sampler and Estimator can now be used with qiskit-terra >= 0.22.0.

IBM Q Provider 0.19.2

No change

Qiskit 0.39.0

This release also officially deprecates the Qiskit Aer project as part of the Qiskit metapackage. This means that in a future release pip install qiskit will no longer include qiskit-aer. If you’re currently installing or listing qiskit as a dependency to get Aer you should upgrade this to explicitly list qiskit-aer as well.

The qiskit-aer project is still active and maintained moving forward but for the Qiskit metapackage (i.e. what gets installed via pip install qiskit) the project is moving towards a model where the Qiskit package only contains the common core functionality for building and compiling quantum circuits, programs, and applications and packages that build on it or link Qiskit to hardware or simulators are separate packages.

Terra 0.22.0

Prelude

The Qiskit Terra 0.22.0 release is a major feature release that includes a myriad of new feature and bugfixes. The highlights for this release are:

• Adding initial support to the transpiler for transpiling QuantumCircuit objects that contain control flow instructions such as ForLoopOp and WhileLoopOp.

• Greatly improved scaling and performance for the transpile() function with large numbers of qubits, especially when optimization_level=3 is used.

• External plugin interface for transpile() that enables external packages to implement stages for the default pass managers. More details on this can be found at qiskit.transpiler.preset_passmanagers.plugin. Additionally, BackendV2 backends can now optionally set custom default plugins to use for the scheduling and translation stages.

• Updated algorithm implementations in qiskit.algorithms that leverage the primitives classes that implement the BaseSampler and BaseEstimator.

New Features

• Add support for representing an operation that has a variable width to the Target class. Previously, a Target object needed to have an instance of Operation defined for each operation supported in the target. This was used for both validation of arguments and parameters of the operation. However, for operations that have a variable width this wasn’t possible because each instance of an Operation class can only have a fixed number of qubits. For cases where a backend supports variable width operations the instruction can be added with the class of the operation instead of an instance. In such cases the operation will be treated as globally supported on all qubits. For example, if building a target like:

  from qiskit.circuit import Parameter, Measure, IfElseOp, ForLoopOp, WhileLoopOp
  from qiskit.circuit.library import IGate, RZGate, SXGate, XGate, CXGate
  from qiskit.transpiler import Target, InstructionProperties
  
  theta = Parameter("theta")
  
  ibm_target = Target()
  i_props = {
      (0,): InstructionProperties(duration=35.5e-9, error=0.000413),
      (1,): InstructionProperties(duration=35.5e-9, error=0.000502),
      (2,): InstructionProperties(duration=35.5e-9, error=0.0004003),
      (3,): InstructionProperties(duration=35.5e-9, error=0.000614),
      (4,): InstructionProperties(duration=35.5e-9, error=0.006149),
  }
  ibm_target.add_instruction(IGate(), i_props)
  rz_props = {
      (0,): InstructionProperties(duration=0, error=0),
      (1,): InstructionProperties(duration=0, error=0),
      (2,): InstructionProperties(duration=0, error=0),
      (3,): InstructionProperties(duration=0, error=0),
      (4,): InstructionProperties(duration=0, error=0),
  }
  ibm_target.add_instruction(RZGate(theta), rz_props)
  sx_props = {
      (0,): InstructionProperties(duration=35.5e-9, error=0.000413),
      (1,): InstructionProperties(duration=35.5e-9, error=0.000502),
      (2,): InstructionProperties(duration=35.5e-9, error=0.0004003),
      (3,): InstructionProperties(duration=35.5e-9, error=0.000614),
      (4,): InstructionProperties(duration=35.5e-9, error=0.006149),
  }
  ibm_target.add_instruction(SXGate(), sx_props)
  x_props = {
      (0,): InstructionProperties(duration=35.5e-9, error=0.000413),
      (1,): InstructionProperties(duration=35.5e-9, error=0.000502),
      (2,): InstructionProperties(duration=35.5e-9, error=0.0004003),
      (3,): InstructionProperties(duration=35.5e-9, error=0.000614),
      (4,): InstructionProperties(duration=35.5e-9, error=0.006149),
  }
  ibm_target.add_instruction(XGate(), x_props)
  cx_props = {
      (3, 4): InstructionProperties(duration=270.22e-9, error=0.00713),
      (4, 3): InstructionProperties(duration=305.77e-9, error=0.00713),
      (3, 1): InstructionProperties(duration=462.22e-9, error=0.00929),
      (1, 3): InstructionProperties(duration=497.77e-9, error=0.00929),
      (1, 2): InstructionProperties(duration=227.55e-9, error=0.00659),
      (2, 1): InstructionProperties(duration=263.11e-9, error=0.00659),
      (0, 1): InstructionProperties(duration=519.11e-9, error=0.01201),
      (1, 0): InstructionProperties(duration=554.66e-9, error=0.01201),
  }
  ibm_target.add_instruction(CXGate(), cx_props)
  measure_props = {
      (0,): InstructionProperties(duration=5.813e-6, error=0.0751),
      (1,): InstructionProperties(duration=5.813e-6, error=0.0225),
      (2,): InstructionProperties(duration=5.813e-6, error=0.0146),
      (3,): InstructionProperties(duration=5.813e-6, error=0.0215),
      (4,): InstructionProperties(duration=5.813e-6, error=0.0333),
  }
  ibm_target.add_instruction(Measure(), measure_props)
  ibm_target.add_instruction(IfElseOp, name="if_else")
  ibm_target.add_instruction(ForLoopOp, name="for_loop")
  ibm_target.add_instruction(WhileLoopOp, name="while_loop")
  

  The IfElseOp, ForLoopOp, and WhileLoopOp operations are globally supported for any number of qubits. This is then reflected by other calls in the Target API such as instruction_supported():

  ibm_target.instruction_supported(operation_class=WhileLoopOp, qargs=(0, 2, 3, 4))
  ibm_target.instruction_supported('if_else', qargs=(0, 1))
  

  both return True.

• Added new primitive implementations, BackendSampler and BackendEstimator, to qiskit.primitives. Thes new primitive class implementation wrap a BackendV1 or BackendV2 instance as a BaseSampler or BaseEstimator respectively. The intended use case for these primitive implementations is to bridge the gap between providers that do not have native primitive implementations and use that provider’s backend with APIs that work with primitives. For example, the SamplingVQE class takes a BaseSampler instance to function. If you’d like to run that class with a backend from a provider without a native primitive implementation you can construct a BackendSampler to do this:

  from qiskit.algorithms.minimum_eigensolvers import SamplingVQE
  from qiskit.algorithms.optimizers import SLSQP
  from qiskit.circuit.library import TwoLocal
  from qiskit.primitives import BackendSampler
  from qiskit.providers.fake_provider import FakeHanoi
  from qiskit.opflow import PauliSumOp
  from qiskit.quantum_info import SparsePauliOp
  
  backend = FakeHanoi()
  sampler = BackendSampler(backend=backend)
  
  operator = PauliSumOp(SparsePauliOp(["ZZ", "IZ", "II"], coeffs=[1, -0.5, 0.12]))
  ansatz = TwoLocal(rotation_blocks=["ry", "rz"], entanglement_blocks="cz")
  optimizer = SLSQP()
  sampling_vqe = SamplingVQE(sampler, ansatz, optimizer)
  result = sampling_vqe.compute_minimum_eigenvalue(operator)
  eigenvalue = result.eigenvalue
  

  If you’re using a provider that has native primitive implementations (such as qiskit-ibm-runtime or qiskit-aer) it is always a better choice to use that native primitive implementation instead of BackendEstimator or BackendSampler as the native implementations will be much more efficient and/or do additional pre and post processing. BackendEstimator and BackendSampler are designed to be generic that can work with any backend that returns Counts in their Results which precludes additional optimization.

• Added a new algorithm class, AdaptVQE to qiskit.algorithms This algorithm uses a qiskit.algorithms.minimum_eigensolvers.VQE in combination with a pool of operators from which to build out an qiskit.circuit.library.EvolvedOperatorAnsatz adaptively. For example:

  from qiskit.algorithms.minimum_eigensolvers import AdaptVQE, VQE
  from qiskit.algorithms.optimizers import SLSQP
  from qiskit.primitives import Estimator
  from qiskit.circuit.library import EvolvedOperatorAnsatz
  
  # get your Hamiltonian
  hamiltonian = ...
  
  # construct your ansatz
  ansatz = EvolvedOperatorAnsatz(...)
  
  vqe = VQE(Estimator(), ansatz, SLSQP())
  
  adapt_vqe = AdaptVQE(vqe)
  
  result = adapt_vqe.compute_minimum_eigenvalue(hamiltonian)
  

• The BackendV2 class now has support for two new optional hook points enabling backends to inject custom compilation steps as part of transpile() and generate_preset_pass_manager(). If a BackendV2 implementation includes the methods get_scheduling_stage_plugin() or get_translation_stage_plugin() the transpiler will use the returned string as the default value for the scheduling_method and translation_method arguments. This enables backends to run additional custom transpiler passes when targetting that backend by leveraging the transpiler stage plugin interface. For more details on how to use this see: Custom Transpiler Passes.

• Added a new keyword argument, ignore_backend_supplied_default_methods, to the transpile() function which can be used to disable a backend’s custom selection of a default method if the target backend has get_scheduling_stage_plugin() or get_translation_stage_plugin() defined.

• Added a label parameter to the Barrier class’s constructor and the barrier() method which allows a user to assign a label to an instance of the Barrier directive. For visualizations generated with circuit_drawer() or QuantumCircuit.draw() this label will be printed at the top of the barrier.

  from qiskit import QuantumCircuit
  
  circuit = QuantumCircuit(2)
  circuit.h(0)
  circuit.h(1)
  circuit.barrier(label="After H")
  circuit.draw('mpl')
  

• Add new gates CCZGate, CSGate, and CSdgGate to the standard gates in the Circuit Library (qiskit.circuit.library).

• Added qiskit.algorithms.eigensolvers package to include interfaces for primitive-enabled algorithms. This new module will eventually replace the previous qiskit.algorithms.eigen_solvers. This new module contains an alternative implementation of the VQD which instead of taking a backend or QuantumInstance instead takes an instance of BaseEstimator, including Estimator, BackendEstimator, or any provider implementations such as those as those present in qiskit-ibm-runtime and qiskit-aer.

  For example, to use the new implementation with an instance of Estimator class:

  from qiskit.algorithms.eigensolvers import VQD
  from qiskit.algorithms.optimizers import SLSQP
  from qiskit.circuit.library import TwoLocal
  from qiskit.primitives import Sampler, Estimator
  from qiskit.algorithms.state_fidelities import ComputeUncompute
  from qiskit.opflow import PauliSumOp
  from qiskit.quantum_info import SparsePauliOp
  
  h2_op = PauliSumOp(SparsePauliOp(
      ["II", "IZ", "ZI", "ZZ", "XX"],
      coeffs=[
          -1.052373245772859,
          0.39793742484318045,
          -0.39793742484318045,
          -0.01128010425623538,
          0.18093119978423156,
      ],
  ))
  
  estimator = Estimator()
  ansatz = TwoLocal(rotation_blocks=["ry", "rz"], entanglement_blocks="cz")
  optimizer = SLSQP()
  fidelity = ComputeUncompute(Sampler())
  
  vqd = VQD(estimator, fidelity, ansatz, optimizer, k=2)
  result = vqd.compute_eigenvalues(h2_op)
  eigenvalues = result.eigenvalues
  

  Note that the evaluated auxillary operators are now obtained via the aux_operators_evaluated field on the results. This will consist of a list or dict of tuples containing the expectation values for these operators, as we well as the metadata from primitive run. aux_operator_eigenvalues is no longer a valid field.

• Added new algorithms to calculate state fidelities/overlaps for pairs of quantum circuits (that can be parametrized). Apart from the base class (BaseStateFidelity) which defines the interface, there is an implementation of the compute-uncompute method that leverages instances of the BaseSampler primitive: qiskit.algorithms.state_fidelities.ComputeUncompute.

  For example:

  import numpy as np
  from qiskit.primitives import Sampler
  from qiskit.algorithms.state_fidelities import ComputeUncompute
  from qiskit.circuit.library import RealAmplitudes
  
  sampler = Sampler(...)
  fidelity = ComputeUncompute(sampler)
  circuit = RealAmplitudes(2)
  values = np.random.random(circuit.num_parameters)
  shift = np.ones_like(values) * 0.01
  
  job = fidelity.run([circuit], [circuit], [values], [values+shift])
  fidelities = job.result().fidelities
  

• Added a new module qiskit.algorithms.gradients that contains classes which are used to compute gradients using the primitive interfaces defined in qiskit.primitives. There are 4 types of gradient classes: Finite Difference, Parameter Shift, Linear Combination of Unitary, and SPSA with implementations that either use an instance of the BaseEstimator interface:

  • ParamShiftEstimatorGradient

  • LinCombEstimatorGradient

  • FiniteDiffEstimatorGradient

  • SPSAEstimatorGradient

  or an instance of the BaseSampler interface:

  • ParamShiftSamplerGradient

  • LinCombSamplerGradient

  • FiniteDiffSamplerGradient

  • SPSASamplerGradient

  The estimator-based gradients compute the gradient of expectation values, while the sampler-based gradients return gradients of the measurement outcomes (also referred to as « probability gradients »).

  For example:

  estimator = Estimator(...)
  gradient = ParamShiftEstimatorGradient(estimator)
  job = gradient.run(circuits, observables, parameters)
  gradients = job.result().gradients
  

• The Grover class has a new keyword argument, sampler which is used to run the algorithm using an instance of the BaseSampler interface to calculate the results. This new argument supersedes the the quantum_instance argument and accordingly, quantum_instance is pending deprecation and will be deprecated and subsequently removed in future releases.

  Example:

  from qiskit import QuantumCircuit
  from qiskit.primitives import Sampler
  from qiskit.algorithms import Grover, AmplificationProblem
  
  sampler = Sampler()
  oracle = QuantumCircuit(2)
  oracle.cz(0, 1)
  problem = AmplificationProblem(oracle, is_good_state=["11"])
  grover = Grover(sampler=sampler)
  result = grover.amplify(problem)
  

• A new option, "formatter.control.fill_waveform" has been added to the pulse drawer (pulse_v2.draw() and Schedule.draw()) style sheets. This option can be used to remove the face color of pulses in the output visualization which allows for drawing pulses only with lines.

  For example:

  from qiskit.visualization.pulse_v2 import IQXStandard
  
  my_style = IQXStandard(
      **{"formatter.control.fill_waveform": False, "formatter.line_width.fill_waveform": 2}
  )
  
  my_sched.draw(style=my_style)
  

• Added a new transpiler pass, ResetAfterMeasureSimplification, which is used to replace a Reset operation after a Measure with a conditional XGate. This pass can be used on backends where a Reset operation is performed by doing a measurement and then a conditional X gate so that this will remove the duplicate implicit Measure from the Reset operation. For example:

  from qiskit import QuantumCircuit
  from qiskit.transpiler.passes import ResetAfterMeasureSimplification
  
  qc = QuantumCircuit(1)
  qc.measure_all()
  qc.reset(0)
  qc.draw('mpl')
  

  result = ResetAfterMeasureSimplification()(qc)
  result.draw('mpl')
  

• Added a new supported value, "reverse_linear" for the entanglement keyword argument to the constructor for the NLocal circuit class. For TwoLocal circuits (which are subclassess of NLocal), if entanglement_blocks="cx" then using entanglement="reverse_linear" provides an equivalent n-qubit circuit as entanglement="full" but with only \(n-1\) CXGate gates, instead of \(\frac{n(n-1)}{2}\).

• ScheduleBlock has been updated so that it can manage unassigned subroutine, in other words, to allow lazy calling of other programs. For example, this enables the following workflow:

  from qiskit import pulse
  
  with pulse.build() as prog:
    pulse.reference("x", "q0")
  
  with pulse.build() as xq0:
    pulse.play(Gaussian(160, 0.1, 40), pulse.DriveChannel(0))
  
  prog.assign_references({("x", "q0"): xq0})
  

  Now a user can create prog without knowing actual implementation of the reference ("x", "q0"), and assign it at a later time for execution. This improves modularity of pulse programs, and thus one can easily write a template pulse program relying on other calibrations.

  To realize this feature, the new pulse instruction (compiler directive) Reference has been added. This instruction is injected into the current builder scope when the reference() command is used. All references defined in the current pulse program can be listed with the references property.

  In addition, every reference is managed with a scope to ease parameter management. scoped_parameters() and search_parameters() have been added to ScheduleBlock. See API documentation for more details.

• Added a new method SparsePauliOp.argsort(), which returns the composition of permutations in the order of sorting by coefficient and sorting by Pauli. By using the weight keyword argument for the method the output can additionally be sorted by the number of non-identity terms in the Pauli, where the set of all Paulis of a given weight are still ordered lexicographically.

• Added a new method SparsePauliOp.sort(), which will first sort the coefficients using numpy’s argsort() and then sort by Pauli, where the Pauli sort takes precedence. If the Pauli sort is the same, it will then be sorted by coefficient. By using the weight keyword argument the output can additionally be sorted by the number of non-identity terms in the Pauli, where the set of all Paulis of a given weight are still ordered lexicographically.

• Added a new keyword argument, wire_order, to the circuit_drawer() function and QuantumCircuit.draw() method which allows arbitrarily reordering both the quantum and classical bits in the output visualization. For example:

  from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
  
  qr = QuantumRegister(4, "q")
  cr = ClassicalRegister(4, "c")
  cr2 = ClassicalRegister(2, "ca")
  circuit = QuantumCircuit(qr, cr, cr2)
  circuit.h(0)
  circuit.h(3)
  circuit.x(1)
  circuit.x(3).c_if(cr, 10)
  circuit.draw('mpl', cregbundle=False, wire_order=[2, 1, 3, 0, 6, 8, 9, 5, 4, 7])
  

• Added support for the CSGate, CSdgGate and CCZGate classes to the constructor for the operator class CNOTDihedral. The input circuits when creating a CNOTDihedral operator will now support circuits using these gates. For example:

  from qiskit import QuantumCircuit
  from qiskit.quantum_info import CNOTDihedral
  
  qc = QuantumCircuit(2)
  qc.t(0)
  qc.cs(0, 1)
  qc.tdg(0)
  operator = CNOTDihedral(qc)
  

• The amplitude estimation algorithm classes:

  • AmplitudeEstimation,

  • FasterAmplitudeEstimation,

  • IterativeAmplitudeEstimation,

  • MaximumLikelihoodAmplitudeEstimation

  Now have a new keyword argument, sampler on their constructor that takes an instance of an object that implements the BaseSampler interface including BackendSampler, Sampler, or any provider implementations such as those as those present in qiskit-ibm-runtime and qiskit-aer. This provides an alternative to using the quantum_instance argument to set the target Backend or QuantumInstance to run the algorithm on. Using a QuantumInstance is pending deprecation and will be deprecated in a future release.

• Added a new class, BackendV2Converter, which is used to wrap a BackendV1 instance in a BackendV2 interface. It enables you to have a BackendV2 instance from any BackendV1. This enables standardizing access patterns on the newer BackendV2 interface even if you still support BackendV1.

• Added a new function convert_to_target() which is used to take a BackendConfiguration, and optionally a BackendProperties and PulseDefaults and create a Target object equivalent to the contents of those objects.

• qiskit.quantum_info.BaseOperator subclasses (such as ScalarOp, SparsePauliOp and PauliList) can now be used with the built-in Python sum() function.

• A new transpiler pass, ConvertConditionsToIfOps was added, which can be used to convert old-style Instruction.c_if()-conditioned instructions into IfElseOp objects. This is to help ease the transition from the old type to the new type for backends. For most users, there is no need to add this to your pass managers, and it is not included in any preset pass managers.

• Refactored gate commutativity analysis into a class CommutationChecker. This class allows you to check (based on matrix multiplication) whether two gates commute or do not commute, and to cache the results (so that a similar check in the future will no longer require matrix multiplication).

  For example we can now do:

  from qiskit.circuit import QuantumRegister, CommutationChecker
  
  comm_checker = CommutationChecker()
  qr = QuantumRegister(4)
  
  res = comm_checker.commute(CXGate(), [qr[1], qr[0]], [], CXGate(), [qr[1], qr[2]], [])
  

  As the two CX gates commute (the first CX gate is over qubits qr[1] and qr[0], and the second CX gate is over qubits qr[1] and qr[2]), we will have that res is True.

  This commutativity checking is over-conservative for conditional and parameterized gates, and may return False even when such gates commute.

• Added a new transpiler pass CommutativeInverseCancellation that cancels pairs of inverse gates exploiting commutation relations between gates. This pass is a generalization of the transpiler pass InverseCancellation as it detects a larger set of inverse gates, and as it takes commutativity into account. The pass also avoids some problems associated with the transpiler pass CommutativeCancellation.

  For example:

  from qiskit.circuit import QuantumCircuit
  from qiskit.transpiler import PassManager
  from qiskit.transpiler.passes import CommutativeInverseCancellation
  
  circuit = QuantumCircuit(2)
  circuit.z(0)
  circuit.x(1)
  circuit.cx(0, 1)
  circuit.z(0)
  circuit.x(1)
  
  passmanager = PassManager(CommutativeInverseCancellation())
  new_circuit = passmanager.run(circuit)
  

  cancels the pair of self-inverse Z-gates, and the pair of self-inverse X-gates (as the relevant gates commute with the CX-gate), producing a circuit consisting of a single CX-gate.

  The inverse checking is over-conservative for conditional and parameterized gates, and may not cancel some of such gates.

• QuantumCircuit.compose() will now accept an operand with classical bits if the base circuit has none itself. The pattern of composing a circuit with measurements onto a quantum-only circuit is now valid. For example:

  from qiskit import QuantumCircuit
  
  base = QuantumCircuit(3)
  terminus = QuantumCircuit(3, 3)
  terminus.measure_all()
  
  # This will now succeed, though it was previously a CircuitError.
  base.compose(terminus)
  

• The DAGCircuit methods depth() and size() have a new recurse keyword argument for use with circuits that contain control-flow operations (such as IfElseOp, WhileLoopOp, and ForLoopOp). By default this is False and will raise an error if control-flow operations are present, to avoid poorly defined results. If set to True, a proxy value that attempts to fairly weigh each control-flow block relative to its condition is returned, even though the depth or size of a concrete run is generally unknowable. See each method’s documentation for how each control-flow operation affects the output.

• DAGCircuit.count_ops() gained a recurse keyword argument for recursing into control-flow blocks. By default this is True, and all operations in all blocks will be returned, as well as the control-flow operations themselves.

• Added an argument create_preds_and_succs to the functions circuit_to_dagdependency() and dag_to_dagdependency() that convert from QuantumCircuit and DAGCircuit, respectively, to DAGDependency. When the value of create_preds_and_succs is False, the transitive predecessors and successors for nodes in DAGDependency are not constructed, making the conversions faster and significantly less memory-intensive. The direct predecessors and successors for nodes in DAGDependency are constructed as usual.

  For example:

  from qiskit.converters import circuit_to_dagdependency
  from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
  
  circuit_in = QuantumCircuit(2)
  circuit_in.h(qr[0])
  circuit_in.h(qr[1])
  
  dag_dependency = circuit_to_dagdependency(circuit_in, create_preds_and_succs=False)
  

• Added new attributes Clifford.symplectic_matrix, Clifford.tableau, Clifford.z, Clifford.x, Clifford.phase, Clifford.stab, Clifford.stab_z, Clifford.stab_x, Clifford.stab_phase, Clifford.destab, Clifford.destab_z, Clifford.destab_x, Clifford.destab_phase to the Clifford class. These can be used instead of Clifford.table, that will be deprecated in the future. StabilizerTable and PauliTable are pending deprecation and will be deprecated in the future release and subsequently removed after that.

• The Commuting2qGateRouter constructor now has a new keyword argument, edge_coloring. This argument is used to provide an edge coloring of the coupling map to determine the order in which the commuting gates are applied.

• Added a new algorithms interface for creating time evolution algorithms using the primitives BaseSampler and BaseEstimator. This new interface consists of:

  • TimeEvolutionProblem

  • TimeEvolutionResult

  • ImaginaryTimeEvolver

  • RealTimeEvolver

  This new interface is an alternative to the previously existing time evolution algorithms interface available defined with EvolutionProblem, EvolutionResult, RealEvolver, and ImaginaryEvolver which worked with a QuantumInstance object instead of primitives. This new interface supersedes the previous interface which will eventually be deprecated and subsequently removed in future releases.

• Added new backend classes to qiskit.providers.fake_provider:

  • FakeAuckland

  • FakeOslo

  • FakeGeneva

  • FakePerth

  These new classes implement the BackendV2 interface and are created using stored snapshots of the backend information from the IBM Quantum systems ibm_auckland, ibm_oslo, ibm_geneva, and ibm_perth systems respectively.

• The Z2Symmetries class has two new methods, convert_clifford() and taper_clifford(). These two methods are the two operations necessary for taperng an operator based on the Z2 symmetries in the object and were previously performed internally via the taper() method. However, these methods are now public methods of the class which can be called individually if needed.

• The runtime performance for conjugation of a long PauliList object by a Clifford using the PauliList.evolve() has significantly improved. It will now run significantly faster than before.

• Added a new abstract class ClassicalIOChannel to the qiskit.pulse.channels module. This class is used to represent classical I/O channels and differentiate these channels from other subclasses of Channel. This new class is the base class for the MemorySlot, RegisterSlot, and SnapshotChannel classes. Accordingly, the pad() canonicalization pulse transform in qiskit.pulse.transforms will not introduce delays to any instances of ClassicalIOChannel

• The SabreSwap transpiler pass has a new keyword argument on its constructor, trials. The trials argument is used to specify the number of random seed trials to attempt. The output from the SABRE algorithm can differ greatly based on the seed used for the random number. SabreSwap will now run the algorithm with trials number of random seeds and pick the best (with the fewest swaps inserted). If trials is not specified the pass will default to use the number of physical CPUs on the local system.

• The SabreLayout transpiler pass has a new keyword argument on its constructor, swap_trials. The swap_trials argument is used to specify how many random seed trials to run on the SabreSwap pass internally. It corresponds to the trials arugment on the SabreSwap pass. When set, each iteration of SabreSwap will be run internally swap_trials times. If swap_trials is not specified the will default to use the number of physical CPUs on the local system.

• Added a new function, estimate_observables() which uses an implementation of the BaseEstimator interface (e.g. Estimator, BackendEstimator, or any provider implementations such as those as those present in qiskit-ibm-runtime and qiskit-aer) to calculate the expectation values, their means and standard deviations from a list or dictionary of observables. This serves a similar purpose to the pre-existing function eval_observables() which performed the calculation using a QuantumInstance object and has been superseded (and will be deprecated and subsequently removed in future releases) by this new function.

• Added a new Operation base class which provides a lightweight abstract interface for objects that can be put on QuantumCircuit. This allows to store « higher-level » objects directly on a circuit (for instance, Clifford objects), to directly combine such objects (for instance, to compose several consecutive Clifford objects over the same qubits), and to synthesize such objects at run time (for instance, to synthesize Clifford in a way that optimizes depth and/or exploits device connectivity). Previously, only subclasses of qiskit.circuit.Instruction could be put on QuantumCircuit, but this interface has become unwieldy and includes too many methods and attributes for general-purpose objects.

  The new Operation interface includes name, num_qubits and num_clbits (in the future this may be slightly adjusted), but importantly does not include definition (and thus does not tie synthesis to the object), does not include condition (this should be part of separate classical control flow), and does not include duration and unit (as these are properties of the output of the transpiler).

  As of now, Operation includes Gate, Reset, Barrier, Measure, and « higher-level » objects such as Clifford. This list of « higher-level » objects will grow in the future.

• A Clifford is now added to a quantum circuit as an Operation, without first synthesizing a subcircuit implementing this Clifford. The actual synthesis is postponed to a later HighLevelSynthesis transpilation pass.

  For example, the following code:

  from qiskit import QuantumCircuit
  from qiskit.quantum_info import random_clifford
  
  qc = QuantumCircuit(3)
  cliff = random_clifford(2)
  qc.append(cliff, [0, 1])
  

  no longer converts cliff to qiskit.circuit.Instruction, which includes synthesizing the clifford into a circuit, when it is appended to qc.

• Added a new transpiler pass OptimizeCliffords that collects blocks of consecutive Clifford objects in a circuit, and replaces each block with a single Clifford.

  For example, the following code:

  from qiskit import QuantumCircuit
  from qiskit.quantum_info import random_clifford
  from qiskit.transpiler.passes import OptimizeCliffords
  from qiskit.transpiler import PassManager
  
  qc = QuantumCircuit(3)
  cliff1 = random_clifford(2)
  cliff2 = random_clifford(2)
  qc.append(cliff1, [2, 1])
  qc.append(cliff2, [2, 1])
  qc_optimized = PassManager(OptimizeCliffords()).run(qc)
  

  first stores the two Cliffords cliff1 and cliff2 on qc as « higher-level » objects, and then the transpiler pass OptimizeCliffords optimizes the circuit by composing these two Cliffords into a single Clifford. Note that the resulting Clifford is still stored on qc as a higher-level object. This pass is not yet included in any of preset pass managers.

• Added a new transpiler pass HighLevelSynthesis that synthesizes higher-level objects (for instance, Clifford objects).

  For example, the following code:

  from qiskit import QuantumCircuit
  from qiskit.quantum_info import random_clifford
  from qiskit.transpiler import PassManager
  from qiskit.transpiler.passes import HighLevelSynthesis
  
  qc = QuantumCircuit(3)
  qc.h(0)
  cliff = random_clifford(2)
  qc.append(cliff, [0, 1])
  
  qc_synthesized = PassManager(HighLevelSynthesis()).run(qc)
  

  will synthesize the higher-level Clifford stored in qc using the default decompose_clifford() function.

  This new transpiler pass HighLevelSynthesis is integrated into the preset pass managers, running right after UnitarySynthesis pass. Thus, transpile() will synthesize all higher-level Cliffords present in the circuit.

  It is important to note that the work done to store Clifford objects as « higher-level » objects and to transpile these objects using HighLevelSynthesis pass should be completely transparent, and no code changes are required.

• SparsePauliOps can now be constructed with coefficient arrays that are general Python objects. This is intended for use with ParameterExpression objects; other objects may work, but do not have first-class support. Some SparsePauliOp methods (such as conversion to other class representations) may not work when using object arrays, if the desired target cannot represent these general arrays.

  For example, a ParameterExpression SparsePauliOp could be constructed by:

  import numpy as np
  from qiskit.circuit import Parameter
  from qiskit.quantum_info import SparsePauliOp
  
  print(SparsePauliOp(["II", "XZ"], np.array([Parameter("a"), Parameter("b")])))
  

  which gives

  SparsePauliOp(['II', 'XZ'],
        coeffs=[ParameterExpression(1.0*a), ParameterExpression(1.0*b)])
  

• Added a new function plot_distribution() for plotting distributions over quasi-probabilities. This is suitable for Counts, QuasiDistribution and ProbDistribution. Raw dict can be passed as well. For example:

  from qiskit.visualization import plot_distribution
  
  quasi_dist = {'0': .98, '1': -.01}
  plot_distribution(quasi_dist)
  

• Introduced a new high level synthesis plugin interface which is used to enable using alternative synthesis techniques included in external packages seamlessly with the HighLevelSynthesis transpiler pass. These alternative synthesis techniques can be specified for any « higher-level » objects of type Operation, as for example for Clifford and LinearFunction objects. This plugin interface is similar to the one for unitary synthesis. In the latter case, the details on writing a new plugin appear in the qiskit.transpiler.passes.synthesis.plugin module documentation.

• Introduced a new class HLSConfig which can be used to specify alternative synthesis algorithms for « higher-level » objects of type Operation. For each higher-level object of interest, an object HLSConfig specifies a list of synthesis methods and their arguments. This object can be passed to HighLevelSynthesis transpiler pass or specified as a parameter hls_config in transpile().

  As an example, let us assume that op_a and op_b are names of two higher-level objects, that op_a-objects have two synthesis methods default which does require any additional parameters and other with two optional integer parameters option_1 and option_2, that op_b-objects have a single synthesis method default, and qc is a quantum circuit containing op_a and op_b objects. The following code snippet:

  hls_config = HLSConfig(op_b=[("other", {"option_1": 7, "option_2": 4})])
  pm = PassManager([HighLevelSynthesis(hls_config=hls_config)])
  transpiled_qc = pm.run(qc)
  

  shows how to run the alternative synthesis method other for op_b-objects, while using the default methods for all other high-level objects, including op_a-objects.

• Added new methods for executing primitives: BaseSampler.run() and BaseEstimator.run(). These methods execute asynchronously and return JobV1 objects which provide a handle to the exections. These new run methods can be passed QuantumCircuit objects (and observables for BaseEstimator) that are not registered in the constructor. For example:

  estimator = Estimator()
  result = estimator.run(circuits, observables, parameter_values).result()
  

  This provides an alternative to the previous execution model (which is now deprecated) for the BaseSampler and BaseEstimator primitives which would take all the inputs via the constructor and calling the primitive object with the combination of those input parameters to use in the execution.

• Added shots option for reference implementations of primitives. Random numbers can be fixed by giving seed_primitive. For example:

  from qiskit.primitives import Sampler
  from qiskit import QuantumCircuit
  
  bell = QuantumCircuit(2)
  bell.h(0)
  bell.cx(0, 1)
  bell.measure_all()
  
  with Sampler(circuits=[bell]) as sampler:
      result = sampler(circuits=[0], shots=1024, seed_primitive=15)
      print([q.binary_probabilities() for q in result.quasi_dists])
  

• The constructors for the BaseSampler and BaseEstimator primitive classes have a new optional keyword argument, options which is used to set the default values for the options exposed via the options attribute.

• Added the PVQD class to the time evolution framework in qiskit.algorithms. This class implements the projected Variational Quantum Dynamics (p-VQD) algorithm Barison et al..

  In each timestep this algorithm computes the next state with a Trotter formula and projects it onto a variational form. The projection is determined by maximizing the fidelity of the Trotter-evolved state and the ansatz, using a classical optimization routine.

  import numpy as np
  
  from qiskit.algorithms.state_fidelities import ComputeUncompute
  from qiskit.algorithms.evolvers import EvolutionProblem
  from qiskit.algorithms.time_evolvers.pvqd import PVQD
  from qiskit.primitives import Estimator, Sampler
  from qiskit import BasicAer
  from qiskit.circuit.library import EfficientSU2
  from qiskit.quantum_info import Pauli, SparsePauliOp
  from qiskit.algorithms.optimizers import L_BFGS_B
  
  sampler = Sampler()
  fidelity = ComputeUncompute(sampler)
  estimator = Estimator()
  hamiltonian = 0.1 * SparsePauliOp([Pauli("ZZ"), Pauli("IX"), Pauli("XI")])
  observable = Pauli("ZZ")
  ansatz = EfficientSU2(2, reps=1)
  initial_parameters = np.zeros(ansatz.num_parameters)
  
  time = 1
  optimizer = L_BFGS_B()
  
  # setup the algorithm
  pvqd = PVQD(
      fidelity,
      ansatz,
      initial_parameters,
      estimator,
      num_timesteps=100,
      optimizer=optimizer,
  )
  
  # specify the evolution problem
  problem = EvolutionProblem(
      hamiltonian, time, aux_operators=[hamiltonian, observable]
  )
  
  # and evolve!
  result = pvqd.evolve(problem)
  

• The QNSPSA.get_fidelity() static method now supports an optional sampler argument which is used to provide an implementation of the BaseSampler interface (such as Sampler, BackendSampler, or any provider implementations such as those present in qiskit-ibm-runtime and qiskit-aer) to compute the fidelity of a QuantumCircuit. For example:

  from qiskit.primitives import Sampler
  from qiskit.algorithms.optimizers import QNSPSA
  
  fidelity = QNSPSA.get_fidelity(my_circuit, Sampler())
  

• Added a new keyword argument sampler to the constructors of the phase estimation classes:

  • IterativePhaseEstimation

  • PhaseEstimation

  • HamiltonianPhaseEstimation

  This argument is used to provide an implementation of the BaseSampler interface such as Sampler, BackendSampler, or any provider implementations such as those as those present in qiskit-ibm-runtime and qiskit-aer.

  For example:

  from qiskit.primitives import Sampler
  from qiskit.algorithms.phase_estimators import HamiltonianPhaseEstimation
  from qiskit.synthesis import MatrixExponential
  from qiskit.quantum_info import SparsePauliOp
  from qiskit.opflow import PauliSumOp
  
  
  sampler = Sampler()
  num_evaluation_qubits = 6
  phase_est = HamiltonianPhaseEstimation(
      num_evaluation_qubits=num_evaluation_qubits, sampler=sampler
  )
  
  hamiltonian = PauliSumOp(SparsePauliOp.from_list([("X", 0.5), ("Y", 0.6), ("I", 0.7)]))
  result = phase_est.estimate(
      hamiltonian=hamiltonian,
      state_preparation=None,
      evolution=MatrixExponential(),
      bound=1.05,
  )
  

• The SabreSwap transpiler pass has significantly improved runtime performance due to a rewrite of the algorithm in Rust.

• Symbolic pulse subclasses Gaussian, GaussianSquare, Drag and Constant have been upgraded to instantiate SymbolicPulse rather than the subclass itself. All parametric pulse objects in pulse programs must be symbolic pulse instances, because subclassing is no longer neccesary. Note that SymbolicPulse can uniquely identify a particular envelope with the symbolic expression object defined in SymbolicPulse.envelope.

• Added a new function, sampled_expectation_value(), that allows for computing expectation values for diagonal operators from distributions such as Counts and QuasiDistribution. Valid operators for use with this function are: str, Pauli, PauliOp, PauliSumOp, and SparsePauliOp.

• A SamplingVQE class is introduced, which is optimized for diagonal hamiltonians and leverages a sampler primitive. A QAOA class is also added that subclasses SamplingVQE.

  To use the new SamplingVQE with a reference primitive, one can do, for example:

  from qiskit.algorithms.minimum_eigensolvers import SamplingVQE
  from qiskit.algorithms.optimizers import SLSQP
  from qiskit.circuit.library import TwoLocal
  from qiskit.primitives import Sampler
  from qiskit.opflow import PauliSumOp
  from qiskit.quantum_info import SparsePauliOp
  
  operator = PauliSumOp(SparsePauliOp(["ZZ", "IZ", "II"], coeffs=[1, -0.5, 0.12]))
  
  sampler = Sampler()
  ansatz = TwoLocal(rotation_blocks=["ry", "rz"], entanglement_blocks="cz")
  optimizer = SLSQP()
  
  sampling_vqe = SamplingVQE(sampler, ansatz, optimizer)
  result = sampling_vqe.compute_minimum_eigenvalue(operator)
  eigenvalue = result.eigenvalue
  

  Note that the evaluated auxillary operators are now obtained via the aux_operators_evaluated field on the results. This will consist of a list or dict of tuples containing the expectation values for these operators, as we well as the metadata from primitive run. aux_operator_eigenvalues is no longer a valid field.

• Added a new atol keyword argument to the SparsePauliOp.equiv() method to adjust to tolerance of the equivalence check,

• Introduced a new plugin interface for transpiler stages which is used to enable alternative PassManager objects from an external package in a particular stage as part of transpile() or the StagedPassManager output from generate_preset_pass_manager(), level_0_pass_manager(), level_1_pass_manager(), level_2_pass_manager(), and level_3_pass_manager(). Users can select a plugin to use for a transpiler stage with the init_method, layout_method, routing_method, translation_method, optimization_method, and scheduling_method keyword arguments on transpile() and generate_preset_pass_manager(). A full list of plugin names currently installed can be found with the list_stage_plugins() function. For creating plugins refer to the qiskit.transpiler.preset_passmanagers.plugin module documentation which includes a guide for writing stage plugins.

• The transpile() has two new keyword arguments, init_method and optimization_method which are used to specify alternative plugins to use for the init stage and optimization stages respectively.

• The PassManagerConfig class has 2 new attributes, init_method and optimization_method along with matching keyword arguments on the constructor methods. These represent the user specified init and optimization plugins to use for compilation.

• The SteppableOptimizer class is added. It allows one to perfore classical optimizations step-by-step using the step() method. These optimizers implement the « ask and tell » interface which (optionally) allows to manually compute the required function or gradient evaluations and plug them back into the optimizer. For more information about this interface see: ask and tell interface. A very simple use case when the user might want to do the optimization step by step is for readout:

  import random
  import numpy as np
  from qiskit.algorithms.optimizers import GradientDescent
  
  def objective(x):
        return (np.linalg.norm(x) - 1) ** 2
  
  def grad(x):
        return 2 * (np.linalg.norm(x) - 1) * x / np.linalg.norm(x)
  
  
  initial_point = np.random.normal(0, 1, size=(100,))
  
  optimizer = GradientDescent(maxiter=20)
  optimizer.start(x0=initial_point, fun=objective, jac=grad)
  
  for _ in range(maxiter):
      state = optimizer.state
      # Here you can manually read out anything from the optimizer state.
      optimizer.step()
  
  result = optimizer.create_result()
  

  A more complex case would be error handling. Imagine that the function you are evaluating has a random chance of failing. In this case you can catch the error and run the function again until it yields the desired result before continuing the optimization process. In this case one would use the ask and tell interface.

  import random
  import numpy as np
  from qiskit.algorithms.optimizers import GradientDescent
  
  def objective(x):
      if random.choice([True, False]):
          return None
      else:
          return (np.linalg.norm(x) - 1) ** 2
  
  def grad(x):
      if random.choice([True, False]):
          return None
      else:
          return 2 * (np.linalg.norm(x) - 1) * x / np.linalg.norm(x)
  
  
  initial_point = np.random.normal(0, 1, size=(100,))
  
  optimizer = GradientDescent(maxiter=20)
  optimizer.start(x0=initial_point, fun=objective, jac=grad)
  
  while optimizer.continue_condition():
      ask_data = optimizer.ask()
      evaluated_gradient = None
  
      while evaluated_gradient is None:
          evaluated_gradient = grad(ask_data.x_center)
          optimizer.state.njev += 1
  
      optmizer.state.nit += 1
  
      cf  = TellData(eval_jac=evaluated_gradient)
      optimizer.tell(ask_data=ask_data, tell_data=tell_data)
  
  result = optimizer.create_result()
  

  Transitioned GradientDescent to be a subclass of SteppableOptimizer.

• The subset_fitter method is added to the TensoredMeasFitter class. The implementation is restricted to mitigation patterns in which each qubit is mitigated individually, e.g. [[0], [1], [2]]. This is, however, the most widely used case. It allows the TensoredMeasFitter to be used in cases where the numberical order of the physical qubits does not match the index of the classical bit.

• Control-flow operations are now supported through the transpiler at optimization levels 0 and 1 (e.g. calling transpile() or generate_preset_pass_manager() with keyword argument optimization_level=1). One can now construct a circuit such as

  from qiskit import QuantumCircuit
  
  qc = QuantumCircuit(2, 1)
  qc.h(0)
  qc.measure(0, 0)
  with qc.if_test((0, True)) as else_:
    qc.x(1)
  with else_:
    qc.y(1)
  

  and successfully transpile this, such as by:

  from qiskit import transpile
  from qiskit_aer import AerSimulator
  
  backend = AerSimulator(method="statevector")
  transpiled = transpile(qc, backend)
  

  The available values for the keyword argument layout_method are « trivial » and « dense ». For routing_method, « stochastic » and « none » are available. Translation (translation_method) can be done using « translator » or « unroller ». Optimization levels 2 and 3 are not yet supported with control flow, nor is circuit scheduling (i.e. providing a value to scheduling_method), though we intend to expand support for these, and the other layout, routing and translation methods in subsequent releases of Qiskit Terra.

  In order for transpilation with control-flow operations to succeed with a backend, the backend must have the requisite control-flow operations in its stated basis. Qiskit Aer, for example, does this. If you simply want to try out such transpilations, consider overriding the basis_gates argument to transpile().

• The following transpiler passes have all been taught to understand control-flow constructs in the form of ControlFlowOp instructions in a circuit:

  Layout-related

  • ApplyLayout

  • DenseLayout

  • EnlargeWithAncilla

  • FullAncillaAllocation

  • SetLayout

  • TrivialLayout

  • VF2Layout

  • VF2PostLayout

  Routing-related

  • CheckGateDirection

  • CheckMap

  • GateDirection

  • StochasticSwap

  Translation-related

  • BasisTranslator

  • ContainsInstruction

  • GatesInBasis

  • UnitarySynthesis

  • Unroll3qOrMore

  • UnrollCustomDefinitions

  • Unroller

  Optimization-related

  • BarrierBeforeFinalMeasurements

  • Depth

  • FixedPoint

  • Size

  • Optimize1qGatesDecomposition

  • CXCancellation

  • RemoveResetInZeroState

  These passes are most commonly used via the preset pass managers (those used internally by transpile() and generate_preset_pass_manager()), but are also available for other uses. These passes will now recurse into control-flow operations where appropriate, updating or analysing the internal blocks.

• Added a new TrotterQRTE class that implements the RealTimeEvolver interface that uses an qiskit.primitives.BaseEstimator to perform the calculation. This new class supersedes the previously available qiskit.algorithms.TrotterQRTE class (which will be deprecated and subsequenty removed in future releases) that used a Backend or QuantumInstance to perform the calculation.

• DAGCircuit.substitute_node_with_dag() now takes propagate_condition as a keyword argument. This defaults to True, which was the previous behavior, and copies any condition on the node to be replaced onto every operation node in the replacement. If set to False, the condition will not be copied, which allows replacement of a conditional node with a sub-DAG that already faithfully implements the condition.

• DAGCircuit.substitute_node_with_dag() can now take a mapping for its wires parameter as well as a sequence. The mapping should map bits in the replacement DAG to the bits in the DAG it is being inserted into. This permits an easier style of construction for callers when the input node has both classical bits and a condition, and the replacement DAG may use these out-of-order.

• Added the qiskit.algorithms.minimum_eigensolvers package to include interfaces for primitive-enabled algorithms. VQE has been refactored in this implementation to leverage primitives.

  To use the new implementation with a reference primitive, one can do, for example:

  from qiskit.algorithms.minimum_eigensolvers import VQE
  from qiskit.algorithms.optimizers import SLSQP
  from qiskit.circuit.library import TwoLocal
  from qiskit.primitives import Estimator
  from qiskit.quantum_info import SparsePauliOp
  
  h2_op = SparsePauliOp(
      ["II", "IZ", "ZI", "ZZ", "XX"],
      coeffs=[
          -1.052373245772859,
          0.39793742484318045,
          -0.39793742484318045,
          -0.01128010425623538,
          0.18093119978423156,
      ],
  )
  
  estimator = Estimator()
  ansatz = TwoLocal(rotation_blocks=["ry", "rz"], entanglement_blocks="cz")
  optimizer = SLSQP()
  
  vqe = VQE(estimator, ansatz, optimizer)
  result = vqe.compute_minimum_eigenvalue(h2_op)
  eigenvalue = result.eigenvalue
  

  Note that the evaluated auxillary operators are now obtained via the aux_operators_evaluated field on the results. This will consist of a list or dict of tuples containing the expectation values for these operators, as we well as the metadata from primitive run. aux_operator_eigenvalues is no longer a valid field.

Upgrade Notes

• For Target objects that only contain globally defined 2 qubit operations without any connectivity constaints the return from the Target.build_coupling_map() method will now return None instead of a CouplingMap object that contains num_qubits nodes and no edges. This change was made to better reflect the actual connectivity constraints of the Target because in this case there are no connectivity constraints on the backend being modeled by the Target, not a lack of connecitvity. If you desire the previous behavior for any reason you can reproduce it by checking for a None return and manually building a coupling map, for example:

  from qiskit.transpiler import Target, CouplingMap
  from qiskit.circuit.library import CXGate
  
  target = Target(num_qubits=3)
  target.add_instruction(CXGate())
  cmap = target.build_coupling_map()
  if cmap is None:
      cmap = CouplingMap()
      for i in range(target.num_qubits):
          cmap.add_physical_qubit(i)
  

• The default value for the entanglement keyword argument on the constructor for the RealAmplitudes and EfficientSU2 classes has changed from "full" to "reverse_linear". This change was made because the output circuit is equivalent but uses only \(n-1\) instead of \(\frac{n(n-1)}{2}\) CXGate gates. If you desire the previous default you can explicity set entanglement="full" when calling either constructor.

• Added a validation check to BaseSampler.run(). It raises an error if there is no classical bit.

• Behavior of the call() pulse builder function has been upgraded. When a ScheduleBlock instance is called by this method, it internally creates a Reference in the current context, and immediately assigns the called program to the reference. Thus, the Call instruction is no longer generated. Along with this change, it is prohibited to call different blocks with the same name argument. Such operation will result in an error.

• For most architectures starting in the following release of Qiskit Terra, 0.23, the tweedledum package will become an optional dependency, instead of a requirement. This is currently used by some classical phase-oracle functions. If your application or library needs this functionality, you may want to prepare by adding tweedledum to your package’s dependencies immediately.

  tweedledum is no longer a requirement on macOS arm64 (M1) with immediate effect in Qiskit Terra 0.22. This is because the provided wheels for this platform are broken, and building from the sdist is not reliable for most people. If you manually install a working version of tweedledum, all the dependent functionality will continue to work.

• The ._layout attribute of the QuantumCircuit object has been changed from storing a Layout object to storing a data class with 2 attributes, initial_layout which contains a Layout object for the initial layout set during compilation and input_qubit_mapping which contains a dictionary mapping qubits to position indices in the original circuit. This change was necessary to provide all the information for a post-transpiled circuit to be able to fully reverse the permutation caused by initial layout in all situations. While this attribute is private and shouldn’t be used externally, it is the only way to track the initial layout through transpile() so the change is being documented in case you’re relying on it. If you have a use case for the _layout attribute that is not being addressed by the Qiskit API please open an issue so we can address this feature gap.

• The constructors for the SetPhase, ShiftPhase, SetFrequency, and ShiftFrequency classes will now raise a PulseError if the value passed in via the channel argument is not an instance of PulseChannel. This change was made to validate the input to the constructors are valid as the instructions are only valid for pulse channels and not other types of channels.

• The plot_histogram() function has been modified to return an actual histogram of discrete binned values. The previous behavior for the function was despite the name to actually generate a visualization of the distribution of the input. Due to this disparity between the name of the function and the behavior the function behavior was changed so it’s actually generating a proper histogram of discrete data now. If you wish to preserve the previous behavior of plotting a probability distribution of the counts data you can leverage the plot_distribution() to generate an equivalent graph. For example, the previous behavior of plot_histogram({'00': 512, '11': 500}) can be re-created with:

  from qiskit.visualization import plot_distribution
  import matplotlib.pyplot as plt
  
  ax = plt.subplot()
  plot_distribution({'00': 512, '11': 500}, ax=ax)
  ax.set_ylabel('Probabilities')
  

• The qiskit.pulse.builder contexts inline and pad have been removed. These were first deprecated in Terra 0.18.0 (July 2021). There is no replacement for inline; one can simply write the pulses in the containing scope. The pad context manager has had no effect since it was deprecated.

• The output from the SabreSwap transpiler pass (including when optimization_level=3 or routing_method or layout_method are set to 'sabre' when calling transpile()) with a fixed seed value may change from previous releases. This is caused by a new random number generator being used as part of the rewrite of the SabreSwap pass in Rust which significantly improved the performance. If you rely on having consistent output you can run the pass in an earlier version of Qiskit and leverage qiskit.qpy to save the circuit and then load it using the current version.

• The Layout.add() behavior when not specifying a physical_bit has changed from previous releases. In previous releases, a new physical bit would be added based on the length of the Layout object. For example if you had a Layout with the physical bits 1 and 3 successive calls to add() would add physical bits 2, 4, 5, 6, etc. While if the physical bits were 2 and 3 then successive calls would add 4, 5, 6, 7, etc. This has changed so that instead Layout.add() will first add any missing physical bits between 0 and the max physical bit contained in the Layout. So for the 1 and 3 example it now adds 0, 2, 4, 5 and for the 2 and 3 example it adds 0, 1, 4, 5 to the Layout. This change was made for both increased predictability of the outcome, and also to fix a class of bugs caused by the unexpected behavior. As physical bits on a backend always are contiguous sequences from 0 to \(n\) adding new bits when there are still unused physical bits could potentially cause the layout to use more bits than available on the backend. If you desire the previous behavior, you can specify the desired physical bit manually when calling Layout.add().

• The deprecated method SparsePauliOp.table attribute has been removed. It was originally deprecated in Qiskit Terra 0.19. Instead the paulis() method should be used.

• Support for returning a PauliTable from the pauli_basis() function has been removed. Similarly, the pauli_list argument on the pauli_basis() function which was used to switch to a PauliList (now the only return type) has been removed. This functionality was deprecated in the Qiskit Terra 0.19 release.

• The fake backend objects FakeJohannesburg, FakeJohannesburgV2, FakeAlmaden, FakeAlmadenV2, FakeSingapore, and FakeSingaporeV2 no longer contain the pulse defaults payloads. This means for the BackendV1 based classes the BackendV1.defaults() method and pulse simulation via BackendV1.run() is no longer available. For BackendV2 based classes the calibration property for instructions in the Target is no longer populated. This change was done because these systems had exceedingly large pulse defaults payloads (in total ~50MB) due to using sampled waveforms instead of parameteric pulse definitions. These three payload files took > 50% of the disk space required to install qiskit-terra. When weighed against the potential value of being able to compile with pulse awareness or pulse simulate these retired devices the file size is not worth the cost. If you require to leverage these properties you can leverage an older version of Qiskit and leverage qpy to transfer circuits from older versions of qiskit into the current release.

• isinstance check with pulse classes Gaussian, GaussianSquare, Drag and Constant will be invalidated because these pulse subclasses are no longer instantiated. They will still work in Terra 0.22, but you should begin transitioning immediately. Instead of using type information, SymbolicPulse.pulse_type should be used. This is assumed to be a unique string identifer for pulse envelopes, and we can use string equality to investigate the pulse types. For example,

  from qiskit.pulse.library import Gaussian
  
  pulse = Gaussian(160, 0.1, 40)
  
  if isinstance(pulse, Gaussian):
    print("This is Gaussian pulse.")
  

  This code should be upgraded to

  from qiskit.pulse.library import Gaussian
  
  pulse = Gaussian(160, 0.1, 40)
  
  if pulse.pulse_type == "Gaussian":
    print("This is Gaussian pulse.")
  

  With the same reason, the class attributes such as pulse.__class__.__name__ should not be accessed to get pulse type information.

• The exception qiskit.exceptions.QiskitIndexError has been removed and no longer exists as per the deprecation notice from qiskit-terra 0.18.0 (released on Jul 12, 2021).

• The deprecated arguments epsilon and factr for the constructor of the L_BFGS_B optimizer class have been removed. These arguments were originally deprecated as part of the 0.18.0 release (released on July 12, 2021). Instead the ftol argument should be used, you can refer to the scipy docs on the optimizer for more detail on the relationship between these arguments.

• The preset pass managers for levels 1 and 2, which will be used when optimization_level=1 or optimization_level=2 with transpile() or generate_preset_pass_manager() and output from level_1_pass_manager() and level_2_pass_manager(), will now use SabreLayout and SabreSwap by default instead of the previous defaults DenseLayout and StochasticSwap. This change was made to improve the output quality of the transpiler, the SabreLayout and SabreSwap combination typically results in fewer SwapGate objects being inserted into the output circuit. If you would like to use the previous default passes you can set layout_method='dense' and routing_method='stochastic' on transpile() or generate_preset_pass_manager() to leverage DenseLayout and StochasticSwap respectively.

• The implicit use of approximation_degree!=1.0 by default in in the transpile() function when optimization_level=3 is set has been disabled. The transpiler should, by default, preserve unitarity of the input up to known transformations such as one-sided permutations and similarity transformations. This was broken by the previous use of approximation_degree=None leading to incorrect results in cases such as Trotterized evolution with many time steps where unitaries were being overly approximated leading to incorrect results. It was decided that transformations that break unitary equivalence should be explicitly activated by the user. If you desire the previous default behavior where synthesized UnitaryGate instructions are approximated up to the error rates of the target backend’s native instructions you can explicitly set approximation_degree=None when calling transpile() with optimization_level=3, for example:

  transpile(circuit, backend, approximation_degree=None, optimization_level=3)
  

• Change the default of maximum number of allowed function evaluations (maxfun) in L_BFGS_B from 1000 to 15000 to match the SciPy default. This number also matches the default number of iterations (maxiter).

• Updated ProbDistribution and QuasiDistribution to store the information of the number of bits if bitstrings without prefix « 0b » are given. ProbDistribution.binary_probabilities() and QuasiDistribution.binary_probabilities() use the stored number of bits as the default value of the number of bits.

• RZXCalibrationBuilder and RZXCalibrationBuilderNoEcho have been upgraded to skip stretching CX gates implemented by non-echoed cross resonance (ECR) sequence to avoid termination of the pass with unexpected errors. These passes take new argument verbose that controls whether the passes warn when this occurs. If verbose=True is set, pass raises user warning when it enconters non-ECR sequence.

• The visualization module qiskit.visualization has seen some internal reorganisation. This should not have affected the public interface, but if you were accessing any internals of the circuit drawers, they may now be in different places. The only parts of the visualization module that are considered public are the components that are documented in this online documentation.

Deprecation Notes

• Importing the names Int1, Int2, classical_function and BooleanExpression directly from qiskit.circuit is deprecated. This is part of the move to make tweedledum an optional dependency rather than a full requirement. Instead, you should import these names from qiskit.circuit.classicalfunction.

• Modules qiskit.algorithms.factorizers and qiskit.algorithms.linear_solvers are deprecated and will be removed in a future release. They are replaced by tutorials in the Qiskit Textbook: Shor HHL

• The random_stabilizer_table() has been deprecated and will be removed in a future release. Instead the random_pauli_list() function should be used.

• The pulse-module function qiskit.pulse.utils.deprecated_functionality is deprecated and will be removed in a future release. This was a primarily internal-only function. The same functionality is supplied by qiskit.utils.deprecate_function, which should be used instead.

• The method of executing primitives has been changed. The BaseSampler.__call__() and BaseEstimator.__call__() methods were deprecated. For example:

  estimator = Estimator(...)
  result = estimator(circuits, observables, parameters)
  
  sampler = Sampler(...)
  result = sampler(circuits, observables, parameters)
  

  should be rewritten as

  estimator = Estimator()
  result = estimator.run(circuits, observables, parameter_values).result()
  
  sampler = Sampler()
  result = sampler.run(circuits, parameter_values).result()
  

  Using primitives as context managers is deprecated. Not all primitives have a context manager available. When available (e.g. in qiskit-ibm-runtime), the session’s context manager provides equivalent functionality.

  circuits, observables, and parameters in the constructor was deprecated. circuits and observables can be passed from run methods. run methods do not support parameters. Users need to resort parameter values by themselves.

• The unused argument qubit_channel_mapping in the RZXCalibrationBuilder and RZXCalibrationBuilderNoEcho transpiler passes have been deprecated and will be removed in a future release. This argument is no longer used and has no effect on the operation of the passes.

Bug Fixes

• Fixed an issue where Pauli.evolve() and PauliList.evolve() would raise a dtype error when evolving by certain Clifford gates which modified the Pauli’s phase. Fixed #8438

• Fixed a bug in QuantumCircuit.initialize() and QuantumCircuit.prepare_state() that caused them to not accept a single Qubit as argument to initialize.

• The method QuantumCircuit.while_loop() will now resolve classical bit references in its condition in the same way that QuantumCircuit.if_test() and InstructionSet.c_if() do.

• The DAGCircuit methods depth(), size() and DAGCircuit.count_ops() would previously silently return results that had little-to-no meaning if control-flow was present in the circuit. The depth() and size() methods will now correctly throw an error in these cases, but have a new recurse keyword argument to allow the calculation of a proxy value, while count_ops() will by default recurse into the blocks and count the operations within them.

• Fixed an issue in the DenseLayout transpiler pass where any loose Qubit objects (i.e. not part of a QuantumRegister) that were part of a QuantumCircuit would not be included in the output Layout that was generated by the pass.

• The Operator.from_circuit() constructor method has been updated so that it can handle the layout output from transpile() and correctly reverse the qubit permutation caused by layout in all cases. Previously, if your transpiled circuit used loose Qubit objects, multiple QuantumRegister objects, or a single QuantumRegister with a name other than "q" the constructor would have failed to create an Operator from the circuit. Fixed #8800.

• Fixed a bug where decomposing an instruction with one qubit and one classical bit containing a single quantum gate failed. Now the following decomposes as expected:

  block = QuantumCircuit(1, 1)
  block.h(0)
  
  circuit = QuantumCircuit(1, 1)
  circuit.append(block, [0], [0])
  
  decomposed = circuit.decompose()
  

• Fixed initialization of empty symplectic matrix in from_symplectic() in PauliList class For example:

  from qiskit.quantum_info.operators import PauliList
  
  x = np.array([], dtype=bool).reshape((1,0))
  z = np.array([], dtype=bool).reshape((1,0))
  pauli_list = PauliList.from_symplectic(x, z)
  

• Fix a problem in the GateDirection transpiler pass for the CZGate. The CZ gate is symmetric, so flipping the qubit arguments is allowed to match the directed coupling map.

• Fixed issues with the DerivativeBase.gradient_wrapper() method when reusing a circuit sampler between the calls and binding nested parameters.

• Fixed an issue in the mpl and latex circuit drawers, when setting the idle_wires option to False when there was a barrier in the circuit would cause the drawers to fail, has been fixed. Fixed #8313

• Fixed an issue in circuit_drawer() and QuantumCircuit.draw() with the latex method where an OSError would be raised on systems whose temporary directories (e.g /tmp) are on a different filesystem than the working directory. Fixes #8542

• Nesting a FlowController inside another in a PassManager could previously cause some transpiler passes to become « forgotten » during transpilation, if the passes returned a new DAGCircuit rather than mutating their input. Nested FlowControllers will now affect the transpilation correctly.

• Comparing QuantumCircuit and DAGCircuits for equality was previously non-deterministic if the circuits contained more than one register of the same type (e.g. two or more QuantumRegisters), sometimes returning False even if the registers were identical. It will now correctly compare circuits with multiple registers.

• The OpenQASM 2 exporter (QuantumCircuit.qasm()) will now correctly define the qubit parameters for UnitaryGate operations that do not affect all the qubits they are defined over. Fixed #8224.

• There were two bugs in the text circuit drawer that were fixed. These appeared when vertical_compression was set to medium, which is the default. The first would sometimes cause text to overwrite other text or gates, and the second would sometimes cause the connections between a gate and its controls to break. See #8588.

• Fixed an issue with the UnitarySynthesis pass where a circuit with 1 qubit gates and a Target input would sometimes fail instead of processing the circuit as expected.

• The GateDirection transpiler pass will now respect the available values for gate parameters when handling parametrised gates with a Target.

• Fixed an issue in the SNOBFIT optimizer class when an internal error would be raised during the execution of the minimize() method if no input bounds where specified. This is now checked at call time to quickly raise a ValueError if required bounds are missing from the minimize() call. Fixes #8580

• Fixed an issue in the output callable from the get_energy_evaluation() method of the VQD class will now correctly call the specified callback when run. Previously the callback would incorrectly not be used in this case. Fixed #8575

• Fixed an issue when circuit_drawer() was used with reverse_bits=True on a circuit without classical bits that would cause a potentially confusing warning about cregbundle to be emitted. Fixed #8690

• The OpenQASM 3 exporter (qiskit.qasm3) will now correctly handle OpenQASM built-ins (such as reset and measure) that have a classical condition applied by c_if(). Previously the condition would have been ignored.

• Fixed an issue with the SPSA class where internally it was trying to batch jobs into even sized batches which would raise an exception if creating even batches was not possible. This has been fixed so it will always batch jobs successfully even if they’re not evenly sized.

• Fixed the behavior of Layout.add() which was potentially causing the output of transpile() to be invalid and contain more Qubits than what was available on the target backend. Fixed: #8667

• Fixed an issue with the state_to_latex() function: passing a latex string to the optional prefix argument of the function would raise an error. Fixed #8460

• The function state_to_latex() produced not valid LaTeX in presence of close-to-zero values, resulting in errors when state_drawer() is called. Fixed #8169.

• GradientDescent will now correctly count the number of iterations, function evaluations and gradient evaluations. Also the documentation now correctly states that the gradient is approximated by a forward finite difference method.

• Fix deprecation warnings in NaturalGradient, which now uses the StandardScaler to scale the data before fitting the model if the normalize parameter is set to True.

Aer 0.11.0

No change

IBM Q Provider 0.19.2

No change

Qiskit 0.38.0

Terra 0.21.2

No change

Aer 0.11.0

Prelude

The Qiskit Aer 0.11.0 release highlights are:

• The migration to a new self-contained Python namespace qiskit_aer

• The introduction of the AerStatevector class

• The introduction of Aer implementations of primitives, Sampler and Estimator

• Introduction of support for running with cuQuantum

New Features

• Added support for BackendV2 to from_backend(). Now it can generate a NoiseModel object from an input BackendV2 instance. When a BackendV2 input is used on from_backend() the two deprecated options, standard_gates and warnings, are gracefully ignored.

• Added Aer implementation of primitives, Sampler and BaseSampler and BaseEstimator interfaces leverage qiskit aer to efficiently perform the computation of the primitive operations. You can refer to the qiskit.primitives docs for a more detailed description of the primitives API.

• Added a shared library to Qiskit Aer that allows external programs to use Aer’s simulation methods. This is an experimental feature and its API may be changed without the deprecation period.

• Added support for M1 macOS systems. Precompiled binaries for supported Python versions >=3.8 on arm64 macOS will now be published on PyPI for this and future releases.

• Added support for cuQuantum, NVIDIA’s APIs for quantum computing, to accelerate statevector, density matrix and unitary simulators by using GPUs. This is experiemental implementation for cuQuantum Beta 2. (0.1.0) cuStateVec APIs are enabled to accelerate instead of Aer’s implementations by building Aer by setting path of cuQuantum to CUSTATEVEC_ROOT. (binary distribution is not available currently.) cuStateVector is enabled by setting device='GPU' and cuStateVec_threshold options. cuStateVec is enabled when number of qubits of input circuit is equal or greater than cuStateVec_threshold.

• Added partial support for running on ppc64le and s390x Linux platforms. This release will start publishing pre-compiled binaries for ppc64le and s390x Linux platforms on all Python versions. However, unlike other supported platforms not all of Qiskit’s upstream dependencies support these platforms yet. So a C/C++ compiler may be required to build and install these dependencies and a simple pip install qiskit-aer with just a working Python environment will not be sufficient to install Qiskit Aer. Additionally, these same constraints prevent us from testing the pre-compiled wheels before publishing them, so the same guarantees around platform support that exist for the other platforms don’t apply to these platforms.

• Allow initialization with a label, that consists of +-rl. Now the following code works:

  import qiskit
  from qiskit_aer import AerSimulator
  
  qc = qiskit.QuantumCircuit(4)
  qc.initialize('+-rl')
  qc.save_statevector()
  
  AerSimulator(method="statevector").run(qc)
  

Known Issues

• When running on Linux s390x platforms (or other big endian platforms) running circuits that contain UnitaryGate operations will not work because of an endianess bug. See #1506 for more details.

Upgrade Notes

• MPI parallelization for large number of qubits is optimized to apply multiple chunk-swaps as all-to-all communication that can decrease data size exchanged over MPI processes. This upgrade improve scalability of parallelization.

• Set default fusion_max_qubit and fusion_threshold depending on the configured method for AerSimulator. Previously, the default values of fusion_max_qubit and fusion_threshold were 5 and 14 respectively for all simulation methods. However, their optimal values depend on running methods. If you depended on the previous defaults you can explicitly set fusion_max_qubit=5 or fusion_threshold=14 to retain the previous default behavior. For example:

  from qiskit_aer import AerSimulator
  
  sim = AerSimulator(method='mps', fusion_max_qubit=5, fusion_threshold=14)
  

• This is update to support cuQuantum 22.5.0.41 including bug fix of thread safety in some cuStateVec APIs. Now Qiskit Aer turns on multi-threading for multi-shots and multi-chunk parallelization when enabling cuStateVec.

• Running qiskit-aer with Python 3.6 is no longer supported. Python >= 3.7 is now required to install and run qiskit-aer.

• The qiskit-aer Python package has moved to be a self-contained namespace, qiskit_aer. Previously, it shared a namespace with qiskit-terra by being qiskit.providers.aer. This was problematic for several reasons, and this release moves away from it. For the time being import qiskit.providers.aer will continue to work and redirect to qiskit_aer automatically. Imports from the legacy qiskit.provider.aer namespace will emit a DeprecationWarning in the future. To avoid any potential issues starting with this release, updating all imports from qiskit.providers.aer to qiskit_aer and from qiskit.Aer to qiskit_aer.Aer is recommended.

• Removed snapshot instructions (such as SnapshotStatevector) which were deprecated since 0.9.0. Applications that use these instructions need to be modified to use corresponding save instructions (such as SaveStatevector).

• Removed the qiskit_aer.extensions module completely. With the removal of the snapshot instructions, this module has become empty and no longer serves a purpose.

• The required version of Qiskit Terra has been bumped to 0.20.0.

Bug Fixes

• Fixes for MPI chunk distribution. Including fix for global indexing for Thrust implementations, fix for cache blocking of non-gate operations. Also savestatevector returns same statevector to all processes (only 1st process received statevector previously.)

• Handles a multiplexer gate as a unitary gate if it has no control qubits. Previously, if a multiplexer gate does not have control qubits, quantum state was not updated.

• Fixes a bug in RelaxationNoisePass where instruction durations were always assumed to be in dt time units, regardless of the actual unit of the isntruction. Now unit conversion is correctly handled for all instruction duration units.

  See #1453 for details.

• Fixed simulation of for loops where the loop parameter was not used in the body of the loop. For example, previously this code would fail, but will now succeed:

  import qiskit
  from qiskit_aer import AerSimulator
  
  qc = qiskit.QuantumCircuit(2)
  with qc.for_loop(range(4)) as i:
      qc.h(0)
      qc.cx(0, 1)
  
  AerSimulator(method="statevector").run(qc)
  

• Fixes a bug in NoiseModel.from_backend() that raised an error when T2 value greater than 2 * T1 was supplied by the backend. After this fix, it becomes to truncate T2 value up to 2 * T1 and issue a user warning if truncates. The bug was introduced at #1391 and, before that, NoiseModel.from_backend() had truncated the T2 value up to 2 * T1 silently.

  See Issue 1464 for details.

• Fix performance regression in noisy simulations due to large increase in serialization overhead for loading noise models from Python into C++ resulting from unintended nested Python multiprocessing calls. See issue 1407 for details.

• This is the fix for Issue #1557. Different seed numbers are generated for each process if seed_simulator option is not set. This fix average seed set in Circuit for all processes to use the same seed number.

• This is a fix of MPI parallelization for multi-chunk parallelization and multi-shot distribution over parallel processes. There were missing distribution configuration that prevents MPI distribution, is now fixed.

• This is fix for cache blocking transpiler and chunk parallelization for GPUs or MPI. This fix fixes issue with qubits which has many control or target qubits (> blocking_qubits). From this fix, only target qubits of the multi-controlled gate is cache blocked in blocking_qubits. But it does not support case if number of target qubits is still larger than blocking_qubits (i.e. large unitary matrix multiplication)

• Fixes a bug in QuantumError.to_dict() where N-qubit circuit instructions where the assembled instruction always applied to qubits [0, ..., N-1] rather than the instruction qubits. This bug also affected device and fake backend noise models.

  See Issue 1415 for details.

• Because a seed was randomly assigned to each circuit if seed_simulator is not set, multi-circuit simulation was not reproducible with another multi-circuit simulation. Users needed to run multiple single-circuit simulation with the seed_simulator which is randomly assigned in the multi-circuit simulation. This fix allows users to reproduce multi-circuit simulation with another multi-circuit simulation by setting seed_simulator of the first circuit in the first multi-circuit simulation. This fix also resolve an issue reported in https://github.com/Qiskit/qiskit-aer/issues/1511, where simulation with parameter-binds returns identical results for each circuit instance.

• Fix performance issue in multi-shots batched optimization for GPU when using Pauli noise. This fix allows multi-threading to runtime noise sampling, and uses nested OpenMP parallelization when using multiple GPUs. This is fix for issue 1473 <https://github.com/Qiskit/qiskit-aer/issues/1473>

• This is the fix for cuStateVec support, fix for build error because of specification change of some APIs of cuStateVec from cuQuantum version 0.40.

• Fixes an issue when while_loop is the tail of QuantumCircuit. while_loop is translated to jump and mark instructions. However, if a while_loop is at the end of a circuit, its mark instruction is truncated wrongly. This fix corrects the truncation algorithm to always remain mark instructions.

IBM Q Provider 0.19.2

No change

Qiskit 0.37.2

Terra 0.21.2

Prelude

Qiskit Terra 0.21.2 is a primarily a bugfix release, and also comes with several improved documentation pages.

Bug Fixes

• aer_simulator_statevector_gpu will now be recognized correctly as statevector method in some function when using Qiskit Aer’s GPU simulators in QuantumInstance and other algorithm runners.

• Fixed the UCGate.inverse() method which previously did not invert the global phase.

• QuantumCircuit.compose() will now function correctly when used with the inplace=True argument within control-flow builder contexts. Previously the instructions would be added outside the control-flow scope. Fixed #8433.

• Fixed a bug where a bound ParameterExpression was not identified as real if symengine was installed and the bound expression was not a plain 1j. For example:

  from qiskit.circuit import Parameter
  
  x = Parameter("x")
  expr = 1j * x
  bound = expr.bind({x: 2})
  print(bound.is_real())  # used to be True, but is now False
  

• Fixed QPY serialisation and deserialisation of ControlledGate with open controls (i.e. those whose ctrl_state is not all ones). Fixed #8549.

• All fake backends in qiskit.providers.fake_provider.backends have been updated to return the corresponding pulse channel objects with the method call of drive_channel(), measure_channel(), acquire_channel(), control_channel().

• Fixed support for running Z2Symmetries.taper() on larger problems. Previously, the method would require a large amount of memory which would typically cause failures for larger problem. As a side effect of this fix the performance has significantly improved.

Aer 0.10.4

No change

IBM Q Provider 0.19.2

No change

Qiskit 0.37.1

Terra 0.21.1

Bug Fixes

• Fixed an issue in QuantumCircuit.decompose() method when passing in a list of Gate classes for the gates_to_decompose argument. If any gates in the circuit had a label set this argument wouldn’t be handled correctly and caused the output decomposition to incorrectly skip gates explicitly in the gates_to_decompose list.

• Fix to_instruction() which previously tried to create a UnitaryGate without exponentiating the operator to evolve. Since this operator is generally not unitary, this raised an error (and if the operator would have been unitary by chance, it would not have been the expected result).

  Now calling to_instruction() correctly produces a gate that implements the time evolution of the operator it holds:

  >>> from qiskit.opflow import EvolvedOp, X
  >>> op = EvolvedOp(0.5 * X)
  >>> op.to_instruction()
  Instruction(
      name='unitary', num_qubits=1, num_clbits=0,
      params=[array([[0.87758256+0.j, 0.-0.47942554j], [0.-0.47942554j, 0.87758256+0.j]])]
  )
  

• Fixed an issue with the marginal_distribution() function: when a numpy array was passed in for the indices argument the function would raise an error. Fixed #8283

• Previously it was not possible to adjoint a CircuitStateFn that has been constructed from a VectorStateFn. That’s because the statevector has been converted to a circuit with the Initialize instruction, which is not unitary. This problem is now fixed by instead using the StatePreparation instruction, which can be used since the state is assumed to start out in the all 0 state.

  For example we can now do:

  from qiskit import QuantumCircuit
  from qiskit.opflow import StateFn
  
  left = StateFn([0, 1])
  left_circuit = left.to_circuit_op().primitive
  
  right_circuit = QuantumCircuit(1)
  right_circuit.x(0)
  
  overlap = left_circuit.inverse().compose(right_circuit)  # this line raised an error before!
  

• Fix a bug in the Optimizer classes where re-constructing a new optimizer instance from a previously exisiting settings reset both the new and previous optimizer settings to the defaults. This notably led to a bug if Optimizer objects were send as input to Qiskit Runtime programs.

  Now optimizer objects are correctly reconstructed:

  >>> from qiskit.algorithms.optimizers import COBYLA
  >>> original = COBYLA(maxiter=1)
  >>> reconstructed = COBYLA(**original.settings)
  >>> reconstructed._options["maxiter"]
  1  # used to be 1000!
  

• Fixed an issue where the limit_amplitude argument on an individual SymbolicPulse or Waveform instance was not properly reflected by parameter validation. In addition, QPY schedule dump() has been fixed to correctly store the limit_amplitude value tied to the instance, rather than saving the global class variable.

• Fix the pairwise entanglement structure for NLocal circuits. This led to a bug in the ZZFeatureMap, where using entanglement="pairwise" raised an error. Now it correctly produces the desired feature map:

  from qiskit.circuit.library import ZZFeatureMap
  encoding = ZZFeatureMap(4, entanglement="pairwise", reps=1)
  print(encoding.decompose().draw())
  

  The above prints:

       ┌───┐┌─────────────┐
  q_0: ┤ H ├┤ P(2.0*x[0]) ├──■────────────────────────────────────■────────────────────────────────────────────
       ├───┤├─────────────┤┌─┴─┐┌──────────────────────────────┐┌─┴─┐
  q_1: ┤ H ├┤ P(2.0*x[1]) ├┤ X ├┤ P(2.0*(π - x[0])*(π - x[1])) ├┤ X ├──■────────────────────────────────────■──
       ├───┤├─────────────┤└───┘└──────────────────────────────┘└───┘┌─┴─┐┌──────────────────────────────┐┌─┴─┐
  q_2: ┤ H ├┤ P(2.0*x[2]) ├──■────────────────────────────────────■──┤ X ├┤ P(2.0*(π - x[1])*(π - x[2])) ├┤ X ├
       ├───┤├─────────────┤┌─┴─┐┌──────────────────────────────┐┌─┴─┐└───┘└──────────────────────────────┘└───┘
  q_3: ┤ H ├┤ P(2.0*x[3]) ├┤ X ├┤ P(2.0*(π - x[2])*(π - x[3])) ├┤ X ├──────────────────────────────────────────
       └───┘└─────────────┘└───┘└──────────────────────────────┘└───┘
  

• Fixed an issue in handling the global phase of the UCGate class.

Aer 0.10.4

No change

IBM Q Provider 0.19.2

No change

Qiskit 0.37.0

This release officially marks the end of support for the Qiskit Ignis project from Qiskit. It was originally deprecated in the 0.33.0 release and as was documented in that release the qiskit-ignis package has been removed from the Qiskit metapackage, which means in that future release pip install qiskit will no longer include qiskit-ignis. However, note because of limitations in python packaging we cannot automatically remove a pre-existing install of qiskit-ignis. If you are upgrading from a previous version it’s recommended that you manually uninstall Qiskit Ignis with pip uninstall qiskit-ignis or install the metapackage in a fresh python environment.

Qiskit Ignis has been supersceded by the Qiskit Experiments project. You can refer to the migration guide for details on how to switch from Qiskit Ignis to Qiskit Experiments.

Terra 0.21.0

Prelude

The Qiskit 0.21.0 release highlights are:

• Support for serialization of a pulse ScheduleBlock via qiskit.qpy. The QPY Format has been updated to version 5 which includes a definition for including the pulse schedules. To support this, a new SymbolicPulse class was introduced to enable defining parametric pulse waveforms via symbolic expressions.

• Improvements to working with preset pass managers. A new function generate_preset_pass_manager() enables easily generating a pass manager equivalent to what transpile() will use internally. Additionally, preset pass managers are now instances of StagedPassManager which makes it easier to modify sections.

• A refactor of the internal data structure of the QuantumCircuit.data attribute. It previously was a list of tuples in the form (instruction, qubits, clbits) and now is a list of CircuitInstruction objects. The CircuitInstruction objects is backwards compatible with the previous tuple based access, however with runtime overhead cost.

Additionally, the transpiler has been improved to enable better quality outputs. This includes the introduction of new passes such as VF2PostLayout and ToqmSwap.

New Features

• Added a new class, qiskit.transpiler.StagedPassManager, which is a PassManager subclass that has a pipeline with defined phases to perform circuit compilation. Each phase is a PassManager object that will get executed in a fixed order. For example:

  from qiskit.transpiler.passes import *
  from qiskit.transpiler import PassManager, StagedPassManager
  
  basis_gates = ['rx', 'ry', 'rxx']
  init = PassManager([UnitarySynthesis(basis_gates, min_qubits=3), Unroll3qOrMore()])
  translate = PassManager([Collect2qBlocks(),
                           ConsolidateBlocks(basis_gates=basis_gates),
                           UnitarySynthesis(basis_gates)])
  
  staged_pm = StagedPassManager(stages=['init', 'translation'], init=init, translation=translate)
  

• Added the methods PauliList.group_commuting() and SparsePauliOp.group_commuting(), which partition these operators into sublists where each element commutes with all the others. For example:

  from qiskit.quantum_info import PauliList, SparsePauliOp
  
  groups = PauliList(["XX", "YY", "IZ", "ZZ"]).group_commuting()
  # 'groups' is [PauliList(['IZ', 'ZZ']), PauliList(['XX', 'YY'])]
  
  op = SparsePauliOp.from_list([("XX", 2), ("YY", 1), ("IZ", 2j), ("ZZ", 1j)])
  groups = op.group_commuting()
  # 'groups' is [
  #     SparsePauliOp(['IZ', 'ZZ'], coeffs=[0.+2.j, 0.+1.j]),
  #     SparsePauliOp(['XX', 'YY'], coeffs=[2.+0.j, 1.+0.j]),
  # ]
  

• Added a new function, marginal_distribution(), which is used to marginalize an input dictionary of bitstrings to an integer (such as Counts). This is similar in functionality to the existing marginal_counts() function with three key differences. The first is that marginal_counts() works with either a counts dictionary or a Results object while marginal_distribution() only works with a dictionary. The second is that marginal_counts() does not respect the order of indices in its indices argument while marginal_distribution() does and will permute the output bits based on the indices order. The third difference is that marginal_distribution() should be faster as its implementation is written in Rust and streamlined for just marginalizing a dictionary input.

• Added the @ (__matmul__) binary operator to BaseOperator subclasses in the qiskit.quantum_info module. This is shorthand to call the classes” dot method (A @ B == A.dot(B)).

• Added a new optional argument, reps, to QuantumCircuit.decompose(), which allows repeated decomposition of the circuit. For example:

  from qiskit import QuantumCircuit
  
  circuit = QuantumCircuit(1)
  circuit.ry(0.5, 0)
  
  # Equivalent to circuit.decompose().decompose()
  circuit.decompose(reps=2)
  
  # decompose 2 times, but only RY gate 2 times and R gate 1 times
  circuit.decompose(gates_to_decompose=['ry','r'], reps=2)
  

• Added a new pulse base class SymbolicPulse. This is a replacement of the conventional ParametricPulse, which will be deprecated. In the new base class, pulse-envelope and parameter-validation functions are represented by symbolic-expression objects. The new class provides self-contained and portable pulse data since these symbolic equations can be easily serialized through symbolic computation libraries.

• Added support for non-Hermitian operators in AerPauliExpectation. This allows the use of Aer’s fast snapshot expectation computations in algorithms such as QEOM.

• Added a new circuit drawing style, textbook, which uses the color scheme of the Qiskit Textbook.

• A new attribute QuantumCircuit.op_start_times is populated when one of scheduling analysis passes is run on the circuit. It can be used to obtain circuit instruction with instruction time, for example:

  from qiskit import QuantumCircuit, transpile
  from qiskit.providers.fake_provider import FakeMontreal
  
  backend = FakeMontreal()
  
  qc = QuantumCircuit(2)
  qc.h(0)
  qc.cx(0, 1)
  
  qct = transpile(
      qc, backend, initial_layout=[0, 1], coupling_map=[[0, 1]], scheduling_method="alap"
  )
  scheduled_insts = list(zip(qct.op_start_times, qct.data))
  

• Added a new method QuantumCircuit.clear() which is used to remove all instructions from a QuantumCircuit.

• Added a new method QuantumCircuit.copy_empty_like() which is used to get a cleared copy of a QuantumCircuit instance. This is logically equivalent to qc.copy().clear(), but significantly faster and more memory-efficient. This is useful when one needs a new empty circuit with all the same resources (qubits, classical bits, metadata, and so on) already added.

• The Target.instruction_supported() method now supports two new keyword arguments, operation_class and parameters. Using these arguments the instruction_supported() method can now be used for checking that a specific operation with parameter values are supported by a Target object. For example, if you want to check if a Target named target supports running a RXGate with \(\theta = \frac{\pi}{2}\) you would do something like:

  from math import pi
  from qiskit.circuit.library import RXGate
  
  target.instruction_supported(operation_class=RXGate, parameters=[pi/2])
  

  which will return True if target supports running RXGate with \(\theta = \frac{\pi}{2}\) and False if it does not.

• Added a Trotterization-based quantum real-time evolution algorithm qiskit.algorithms.TrotterQRTE. It is compliant with the new quantum time evolution framework and makes use of the ProductFormula and PauliEvolutionGate implementations.

  from qiskit.algorithms import EvolutionProblem
  from qiskit.algorithms.evolvers.trotterization import TrotterQRTE
  from qiskit.opflow import X, Z, StateFn, SummedOp
  
  operator = SummedOp([X, Z])
  initial_state = StateFn([1, 0])
  time = 1
  evolution_problem = EvolutionProblem(operator, time, initial_state)
  
  trotter_qrte = TrotterQRTE()
  evolution_result = trotter_qrte.evolve(evolution_problem)
  evolved_state_circuit = evolution_result.evolved_state
  

• Added a new function generate_preset_pass_manager() which can be used to quickly generate a preset PassManager object that mirrors the PassManager used internally by the transpile() function. For example:

  from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
  from qiskit.providers.fake_provider import FakeWashingtonV2
  
  # Generate an optimization level 3 pass manager targeting FakeWashingtonV2
  pass_manager = generate_preset_pass_manager(3, FakeWashingtonV2())
  

• Added a new function marginal_memory() which is used to marginalize shot memory arrays. Provided with the shot memory array and the indices of interest, the function will return a maginized shot memory array. This function differs from the memory support in the marginal_counts() method which only works on the memory field in a Results object.

• The primitives interface has been extended to accept objects in addition to indices as arguments to the __call__ method. The parameter_values argument can now be optional.

• The OpenQASM 3 exporter (qiskit.qasm3) now supports exporting circuits with explicit delays, such as from QuantumCircuit.delay().

• Added a new layout and routing method to transpile() based on the paper « Time-optimal qubit mapping ». To use it, the optional package Qiskit TOQM must be installed. The routing_method kwarg of transpile() supports an additional value, 'toqm' which is used to enable layout and routing via TOQM.

  To install qiskit-toqm along with Terra, run:

  pip install qiskit-terra[toqm]
  

• Added a new module qiskit.quantum_info.synthesis.qsd to apply Quantum Shannon Decomposition of arbitrary unitaries. This functionality replaces the previous isometry-based approach in the default unitary synthesis transpiler pass as well as when adding unitaries to a circuit using a UnitaryGate.

  The Quantum Shannon Decomposition uses about half the cnot gates as the isometry implementation when decomposing unitary matrices of greater than two qubits.

• Classes in the quantum_info module that support scalar multiplication can now be multiplied by a scalar from either the left or the right. Previously, they would only accept scalar multipliers from the left.

• The transpiler pass LookaheadSwap (used by transpile() when routing_method="lookahead") has seen some performance improvements and will now be approximately three times as fast. This is purely being more efficient in its calculations, and does not change the complexity of the algorithm. In most cases, a more modern routing algorithm like SabreSwap (routing_method="sabre") will be vastly more performant.

• New transpiler passes have been added. The transpiler pass Commuting2qGateRouter uses swap strategies to route a block of commuting gates to the coupling map. Indeed, routing is a hard problem but is significantly easier when the gates commute as in CZ networks. Blocks of commuting gates are also typically found in QAOA. Such cases can be dealt with using swap strategies that apply a predefined set of layers of SWAP gates. Furthermore, the new transpiler pass FindCommutingPauliEvolutions identifies blocks of Pauli evolutions made of commuting two-qubit terms. Here, a swap strategy is specified by the class SwapStrategy. Swap strategies need to be tailored to the coupling map and, ideally, the circuit for the best results.

• Introduced a new optimizer to Qiskit library, which adds support to the optimization of parameters of variational quantum algorithms. This is the Univariate Marginal Distribution Algorithm (UMDA), which is a specific type of the Estimation of Distribution Algorithms. For example:

  from qiskit.opflow import X, Z, I
  from qiskit import Aer
  from qiskit.algorithms.optimizers import UMDA
  from qiskit.algorithms import QAOA
  from qiskit.utils import QuantumInstance
  
  H2_op = (-1.052373245772859 * I ^ I) + \
          (0.39793742484318045 * I ^ Z) + \
          (-0.39793742484318045 * Z ^ I) + \
          (-0.01128010425623538 * Z ^ Z) + \
          (0.18093119978423156 * X ^ X)
  
  p = 2  # Toy example: 2 layers with 2 parameters in each layer: 4 variables
  
  opt = UMDA(maxiter=100, size_gen=20)
  backend = Aer.get_backend('statevector_simulator')
  vqe = QAOA(opt,
             quantum_instance=QuantumInstance(backend=backend),
             reps=p)
  
  result = vqe.compute_minimum_eigenvalue(operator=H2_op)
  

• The constructor for the Unroll3qOrMore transpiler pass has two new optional keyword arguments, target and basis_gates. These options enable you to specify the Target or supported basis gates respectively to describe the target backend. If any of the operations in the circuit are in the target or basis_gates those will not be unrolled by the pass as the target device has native support for the operation.

• QPY serialization has been upgraded to support ScheduleBlock. Now you can save pulse program in binary and load it at later time:

  from qiskit import pulse, qpy
  
  with pulse.build() as schedule:
      pulse.play(pulse.Gaussian(160, 0.1, 40), pulse.DriveChannel(0))
  
  with open('schedule.qpy', 'wb') as fd:
      qpy.dump(schedule, fd)
  
  with open('schedule.qpy', 'rb') as fd:
      new_schedule = qpy.load(fd)[0]
  

  This uses the QPY interface common to QuantumCircuit. See SCHEDULE_BLOCK for details of data structure.

• Added a new transpiler pass, VF2PostLayout. This pass is of a new type to perform a new phase/function in the compilation pipeline, post-layout or post optimization qubit selection. The idea behind this pass is after we finish the optimization loop in transpiler we know what the final gate counts will be on each qubit in the circuit so we can potentially find a better-performing subset of qubits on a backend to execute the circuit. The pass will search for an isomorphic subgraph in the connectivity graph of the target backend and look at the full error rate of the complete circuit on any subgraph found and return the layout found with the lowest error rate for the circuit.

  This pass is similar to the VF2Layout pass and both internally use the same VF2 implementation from retworkx. However, VF2PostLayout is deisgned to run after initial layout, routing, basis translation, and any optimization passes run and will only work if a layout has already been applied, the circuit has been routed, and all gates are in the target basis. This is required so that when a new layout is applied the circuit can still be run on the target device. VF2Layout on the other hand is designed to find a perfect initial layout and can work with any circuit.

• The ApplyLayout transpiler pass now has support for updating a layout on a circuit after a layout has been applied once before. If the post_layout field is present (in addition to the required layout field) the property_set when the ApplyLayout pass is run the pass will update the layout to apply the new layout. This will return a DAGCircuit with the qubits in the new physical order and the layout property set will be updated so that it maps the virtual qubits from the original layout to the physical qubits in the new post_layout field.

• The preset pass managers generated by level_1_pass_manager(), level_2_pass_manager(), and level_3_pass_manager() which correspond to optimization_level 1, 2, and 3 respectively on the transpile() function now run the VF2PostLayout pass after running the routing pass. This enables the transpiler to potentially find a different set of physical qubits on the target backend to run the circuit on which have lower error rates. The VF2PostLayout pass will not be run if you manually specify a layout_method, routing_method, or initial_layout arguments to transpile(). If the pass can find a better performing subset of qubits on backend to run the physical circuit it will adjust the layout of the circuit to use the alternative qubits instead.

• The algorithm iteratively computes each eigenstate by starting from the ground state (which is computed as in VQE) and then optimising a modified cost function that tries to compute eigen states that are orthogonal to the states