Japanese
言語
English
Bengali
French
German
Japanese
Korean
Portuguese
Spanish
Tamil

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

0.41.0

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

注釈

For the 0.7.0, 0.7.1, and 0.7.2 meta-package releases the Qiskit バージョン管理 policy was not formalized yet.

Notable Changes

Qiskit 0.41.0

Terra 0.23.1

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:

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 2022), at which point you will need Python 3.8 or newer to use Qiskit.

New Features
  • 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).

  • 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 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 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:

    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 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, png, hires.png, pdf)

    _images/release_notes-2.png
  • 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 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, png, hires.png, pdf)

    _images/release_notes-3.png
  • 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 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:

    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 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.

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 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 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 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 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 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 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
  • 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 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.

  • 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:

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')
    
    _images/release_notes_0_0.png
  • 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
    
  • 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')
    
    _images/release_notes_1_0.png
    result = ResetAfterMeasureSimplification()(qc)
    result.draw('mpl')
    
    _images/release_notes_2_0.png
  • 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])
    
    _images/release_notes_3_0.png
  • 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)
    
  • 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)
    
  • 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.

  • 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.

  • 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)
    
    _images/release_notes_4_0.png
  • 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:

    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.

  • 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,

  • 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().

  • 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')
    
    Text(0, 0.5, 'Probabilities')
    
    _images/release_notes_5_1.png
  • 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 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).

  • 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 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
  • 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.

  • 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:

New Features
  • 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.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.

  • 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 computed in the previous iterations and have the lowest energy when computed over the ansatz. The interface implemented is very similar to that of VQE and is of the form:

    from qiskit.algorithms import VQD
    from qiskit.utils import QuantumInstance
    from qiskit.circuit.library import TwoLocal
    from qiskit.algorithms.optimizers import COBYLA
    from qiskit import BasicAer
    from qiskit.opflow import I,Z,X
    
    h2_op = (
        -1.052373245772859 * (I ^ I)
        + 0.39793742484318045 * (I ^ Z)
        - 0.39793742484318045 * (Z ^ I)
        - 0.01128010425623538 * (Z ^ Z)
        + 0.18093119978423156 * (X ^ X)
    )
    
    vqd = VQD(k =2, ansatz = TwoLocal(rotation_blocks="ry", entanglement_blocks="cz"),optimizer = COBYLA(maxiter = 0), quantum_instance = QuantumInstance(
                BasicAer.get_backend("qasm_simulator"), shots = 2048)
            )
    vqd_res = vqd.compute_eigenvalues(op)
    

    This particular code snippet generates 2 eigenvalues (ground and 1st excited state) Tests have also been implemented.

Upgrade Notes
  • The data type of each element in QuantumCircuit.data has changed. It used to be a simple 3-tuple of an Instruction, a list of Qubits, and a list of Clbits, whereas it is now an instance of CircuitInstruction.

    The attributes of this new class are operation, qubits and clbits, corresponding to the elements of the previous tuple. However, qubits and clbits are now tuple instances, not lists.

    This new class will behave exactly like the old 3-tuple if one attempts to access its index its elements, or iterate through it. This includes casting the qubits and clbits elements to lists. This is to assist backwards compatibility. Starting from Qiskit Terra 0.21, this is no longer the preferred way to access these elements. Instead, you should use the attribute-access form described above.

    This has been done to allow further developments of the QuantumCircuit data structure in Terra, without constantly breaking backwards compatibility. Planned developments include dynamic parameterized circuits, and an overall reduction in memory usage of deep circuits.

  • The python-constraint dependency, which is used solely by the CSPLayout transpiler pass, is no longer in the requirements list for the Qiskit Terra package. This is because the CSPLayout pass is no longer used by default in any of the preset pass managers for transpile(). While the pass is still available, if you’re using it you will need to manually install python-contraint or when you install qiskit-terra you can use the csp-layout extra, for example:

    pip install "qiskit-terra[csp-layout]"
    
  • The QPY version format version emitted by qpy.dump() has been increased to version 5. 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 5. This change was necessary to fix support for representing controlled gates properly and representing non-default control states.

  • Qiskit Terra’s compiled Rust extensions now have a minimum supported Rust version (MSRV) of 1.56.1. This means when building Qiskit Terra from source the oldest version of the Rust compiler supported is 1.56.1. If you are using an older version of the Rust compiler you will need to update to a newer version to continue to build Qiskit from source. This change was necessary as a number of upstream dependencies have updated their minimum supported versions too.

  • Circuit scheduling now executes in parallel when more than one circuit is provided to schedule(). Refer to #2695 for more details.

  • The previously deprecated BaseBackend, BaseJob, and BaseProvider classes have all been removed. They were originally deprecated in the 0.18.0 release. Instead of these classes you should be using the versioned providers interface classes, the latest being BackendV2, JobV1, and ProviderV1.

  • The previously deprecated backend argument for the constructor of the RZXCalibrationBuilder transpiler pass has been removed. It was originally deprecated in the 0.19.0 release. Instead you should query the Backend object for the instruction_schedule_map and qubit_channel_mapping and pass that directly to the constructor. For example, with a BackendV1 backend:

    from qiskit.transpiler.passes import RZXCalibrationBuilder
    from qiskit.providers.fake_provider import FakeMumbai
    
    backend = FakeMumbai()
    inst_map = backend.defaults().instruction_schedule_map
    channel_map = backend.configuration().qubit_channel_mapping
    cal_pass = RZXCalibrationBuilder(
        instruction_schedule_map=inst_map,
        qubit_channel_mapping=channel_map,
    )
    

    or with a BackendV2 backend:

    from qiskit.transpiler.passes import RZXCalibrationBuilder
    from qiskit.providers.fake_provider import FakeMumbaiV2
    
    backend = FakeMumbaiV2()
    inst_map = backend.instruction_schedule_map
    channel_map = {bit: backend.drive_channel(bit) for bit in range(backend.num_qubits)}
    cal_pass = RZXCalibrationBuilder(
        instruction_schedule_map=inst_map,
        qubit_channel_mapping=channel_map,
    )
    
  • The measurement shot limit for the BasicAer backend has been removed.

  • For the DAGNode, the previously deprecated type, op, qargs, cargs, and wire kwargs and attributes have been removed. These were originally deprecated in the 0.19.0 release. The op, qargs, and cargs kwargs and attributes can be accessed only on instances of DAGOpNode, and the wire kwarg and attribute are only on instances of DAGInNode or DAGOutNode.

  • The deprecated function pauli_group() has been removed. It was originally deprecated in Qiskit Terra 0.17.

  • Several deprecated methods on Pauli have been removed, which were originally deprecated in Qiskit Terra 0.17. These were:

    sgn_prod

    Use Pauli.compose() or Pauli.dot() instead.

    to_spmatrix

    Use Pauli.to_matrix() with argument sparse=True instead.

    kron

    Use Pauli.expand(), but beware that this returns a new object, rather than mutating the existing one.

    update_z and update_x

    Set the z and x attributes of the object directly.

    insert_paulis

    Use Pauli.insert().

    append_paulis

    Use Pauli.expand().

    delete_qubits

    Use Pauli.delete().

    pauli_single

    Construct the label manually and pass directly to the initializer, such as:

    Pauli("I" * index + pauli_label + "I" * (num_qubits - index - len(pauli_label)))
    
    random

    Use quantum_info.random_pauli() instead.

  • Removed the optimize method from the Optimizer classes, which is superseded by the minimize() method as direct replacement. The one exception is SPSA, where the deprecation warning was not triggered so the method there is still kept.

  • Result was modified so that it always contains date, status, and header attributes (set to None if not specified).

  • For Python 3.7 shared-memory38 is now a dependency. This was added as a dependency for Python 3.7 to enable leveraging the shared memory constructs in the standard library of newer versions of Python. If you’re running on Python >= 3.8 there is no extra dependency required.

  • Instruction labels are now type-checked on instruction creation.

  • The preset pass managers generated by level_1_pass_manager(), level_2_pass_manager(), and level_3_pass_manager() and used by the transpile() function’s optimization_level argument at 1, 2, and 3 respectively no longer set a hard time limit on the VF2Layout transpiler pass. This means that the pass will no longer stop trying to find a better alternative perfect layout up until a fixed time limit (100ms for level 1, 10 sec for level 2, and 60 sec for level 3) as doing this limited the reproducibility of compilation when a perfect layout was available. This means that the output when using the pass might be different than before, although in all cases it would only change if a lower noise set of qubits can be found over the previous output. If you wish to retain the previous behavior you can create a custom PassManager that sets the time_limit argument on the constructor for the VF2Layout pass.

Deprecation Notes
  • Calling timeline_drawer() with an unscheduled circuit has been deprecated. All circuits, even one consisting only of delay instructions, must be transpiled with the scheduling_method keyword argument of transpile() set, to generate schedule information being stored in QuantumCircuit.op_start_times.

  • The NetworkX converter functions for the DAGCircuit.to_networkx() and from_networkx(), along with the DAGDependency.to_networkx() method have been deprecated and will be removed in a future release. Qiskit has been using retworkx as its graph library since the qiskit-terra 0.12.0 release, and since then the networkx converter functions have been lossy. They were originally added so that users could leverage functionality in NetworkX’s algorithms library not present in retworkx. Since that time, retworkx has matured and offers more functionality, and the DAGCircuit is tightly coupled to retworkx for its operation. Having these converter methods provides limited value moving forward and are therefore going to be removed in a future release.

  • Accessing several old toggles (HAS_MATPLOTLIB, HAS_PDFTOCAIRO, HAS_PYLATEX and HAS_PIL) from the qiskit.visualization module is now deprecated, and these import paths will be removed in a future version of Qiskit Terra. The same objects should instead be accessed through qiskit.utils.optionals, which contains testers for almost all of Terra’s optional dependencies.

  • The qiskit.test.mock module is now deprecated. The fake backend and fake provider classes which were previously available in qiskit.test.mock have been accessible in qiskit.providers.fake_provider since Terra 0.20.0. This change represents a proper commitment to support the fake backend classes as part of Qiskit, whereas previously they were just part of the internal testing suite, and were exposed to users as a side effect.

  • The arguments』 names when calling an Estimator or Sampler object as a function are renamed from circuit_indices and observable_indices to circuits and observables.

  • The qobj_id and qobj_header keyword arguments for the execute() function have been deprecated and will be removed in a future release. Since the removal of the BaseBackend class these arguments don’t have any effect as no backend supports execution with a Qobj object directly and instead only work with QuantumCircuit objects directly.

  • The arguments x, z and label to the initializer of Pauli were documented as deprecated in Qiskit Terra 0.17, but a bug prevented the expected warning from being shown at runtime. The warning will now correctly show, and the arguments will be removed in Qiskit Terra 0.23 or later. A pair of x and z should be passed positionally as a single tuple (Pauli((z, x))). A string label should be passed positionally in the first argument (Pauli("XYZ")).

  • The SPSA.optimize() method is deprecated in favor of SPSA.minimize(), which can be used as direct replacement. Note that this method returns a complete result object with more information than before available.

  • The circuits argument of qpy.dump() has been deprecated and replaced with programs since now QPY supports multiple data types other than circuits.

  • AlignmentKind.to_dict() method has been deprecated and will be removed.

Bug Fixes
  • Extra validation was added to DiagonalGate to check the argument has modulus one.

  • Duplicate qubit indices given to SparsePauliOp.from_sparse_list() will now correctly raise an error, instead of silently overwriting previous values. The old behavior can be accessed by passing the new keyword argument do_checks=False.

  • The timeline_drawer() visualization will no longer misalign classical register slots.

  • Parameter validation for GaussianSquare is now consistent before and after construction. Refer to #7882 for more details.

  • Fixed a bug in which the LaTeX statevector drawer ignored the max_size parameter.

  • Fixed support for QPY serialization (qpy.dump()) and deserialization (qpy.load()) of a QuantumCircuit object containing controlled gates with an open control state. Previously, the open control state would be lost by the serialization process and the reconstructed circuit.

  • Fixed QuantumCircuit.reverse_bits() with circuits containing registerless Qubit and Clbit. For example, the following will now work:

    from qiskit.circuit import QuantumCircuit, Qubit, Clbit
    
    qc = QuantumCircuit([Qubit(), Clbit()])
    qc.h(0).c_if(qc.clbits[0], 0)
    qc.reverse_bits()
    
  • Fixed the ConfigurableFakeBackend.t2 attribute, which was previously incorrectly set based on the provided t1 value.

  • Fixed a bug in plot_histogram() when the number_to_keep argument was smaller that the number of keys. The following code will no longer throw errors and will be properly aligned:

    from qiskit.visualization import plot_histogram
    data = {'00': 3, '01': 5, '11': 8, '10': 11}
    plot_histogram(data, number_to_keep=2)
    
  • Improved the performance of building and working with parameterized QuantumCircuit instances with many gates that share a relatively small number of parameters.

  • The OpenQASM 3 exporter (qiskit.qasm3) will no longer attempt to produce definitions for non-standard gates in the basis_gates option.

  • Fixed the getter of OptimizerResult.nit, which previously returned the number of Jacobian evaluations instead of the number of iterations.

  • Fixed a bug in the string representation of Result objects that caused the attributes to be specified incorrectly.

  • Fixed an issue with transpile() where in some cases providing a list of basis gate strings with the basis_gates keyword argument or implicitly via a Target input via the target keyword argument would not be interpreted correctly and result in a subset of the listed gates being used for each circuit.

  • Fixed an issue in the UnitarySynthesis transpiler pass which would result in an error when a Target that didn’t have any qubit restrictions on the operations (e.g. in the case of an ideal simulator target) was specified with the target keyword argument for the constructor.

  • The method qiskit.result.marginal_counts(), when passed a Result from a pulse backend, would fail, because it contains an array of ExperimentResult objects, each of which have an QobjExperimentHeader, and those ExperimentHeaders lack creg_sizes instance-variables. If the Result came from a simulator backend (e.g. Aer), that instance-variable would be there. We fix marginal_counts so that it skips logic that needs creg_sizes if the field is not present, or non-None.

  • 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.

  • Fixed an issue with reproducibility of the transpile() function when running with optimization_level 1, 2, and 3. Previously, under some conditions when there were multiple perfect layouts (a layout that doesn’t require any SWAP gates) available the selected layout and output circuit could vary regardless of whether the seed_transpiler argument was set.

Aer 0.10.4

No change

IBM Q Provider 0.19.2

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.

  • 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).

Qiskit 0.36.2

Terra 0.20.2

Prelude

Qiskit Terra 0.20.2 is a bugfix release, addressing some minor issues identified since the last patch release.

Bug Fixes
  • Fixed an issue with BackendV2-based fake backend classes from the qiskit.providers.fake_provider module such as FakeMontrealV2, where the values for the dtm and dt attributes and the associated attribute Target.dt would not be properly converted to seconds. This would cause issues when using these fake backends with scheduling. See #8018.

  • marginal_counts() will now succeed when asked to marginalize memory with an indices parameter containing non-zero elements. Previously, shots whose hexadecimal result representation was sufficiently small could raise a ValueError. See #8044.

  • The OpenQASM 3 exporter (qiskit.qasm3) will now output input or output declarations before gate declarations. This is more consistent with the current reference ANTLR grammar from the OpenQASM 3 team. See #7964.

  • Fixed a bug in the RZXCalibrationBuilder transpiler pass where the scaled cross-resonance pulse amplitude could appear to be parametrized even after assignment. This could cause the pulse visualization tools to use the parametrized format instead of the expected numeric one. See #8031.

  • Fixed an issue with the transpile() function when run with a BackendV2-based backend and setting the scheduling_method keyword argument. Previously, the function would not correctly process the default durations of the instructions supported by the backend which would lead to an error.

  • Fixed a bug in the RZXCalibrationBuilder transpiler pass that was causing pulses to sometimes be constructed with incorrect durations. See #7994.

  • The SabreSwap transpiler pass, used in transpile() when routing_method="sabre" is set, will no longer sporadically drop classically conditioned gates and their successors from circuits during the routing phase of transpilation. See #8040.

  • Statevector will now allow direct iteration through its values (such as for coefficient in statevector) and correctly report its length under len. Previously it would try and and access out-of-bounds data and raise a QiskitError. See #8039.

Aer 0.10.4

No change

Ignis 0.7.1

Prelude

This is a bugfix release that primarily fixes a packaging issue that was causing the docs/ directory, which contains the source files used to build the qiskit-ignis documentation, to get included in the Python package.

IBM Q Provider 0.19.1

No change

Qiskit 0.36.1

Terra 0.20.1

Prelude

Qiskit Terra 0.20.1 is a bugfix release resolving issues identified in release 0.20.0.

Known Issues
  • QPY deserialization with the qpy.load() function of a directly instantiated UCPauliRotGate object in a circuit will fail because the rotation axis argument to the class isn’t stored in a standard place. To workaround this you can instead use the subclasses: UCRXGate, UCRYGate, or UCRZGate (based on whether you’re using a rotation axis of "X", "Y", or "Z" respectively) which embeds the rotation axis in the class constructor and will work correctly in QPY.

  • Since its original introduction in Qiskit Terra 0.20, XXPlusYYGate has used a negative angle convention compared to all other rotation gates. In Qiskit Terra 0.21, this will be corrected to be consistent with the other rotation gates. This does not affect any other rotation gates, nor XXMinusYYGate.

Bug Fixes
  • Fixed Clifford, Pauli and CNOTDihedral operator initialization from compatible circuits that contain Delay instructions. These instructions are treated as identities when converting to operators.

  • Fixed an issue where the eval_observables() function would raise an error if its quantum_state argument was of type StateFn. eval_observables now correctly supports all input types denoted by its type hints.

  • Fixed an issue with the visualization function dag_drawer() and method DAGCircuit.draw() where previously the drawer would fail when attempting to generate a visualization for a DAGCircuit object that contained a Qubit or Clbit which wasn’t part of a QuantumRegister or ClassicalRegister. Fixed #7915.

  • Fixed parameter validation for class Drag. Previously, it was not sensitive to large beta values with negative signs, which may have resulted in waveform samples with a maximum value exceeding the amplitude limit of 1.0.

  • The QuantumInstance class used by many algorithms (like VQE) was hard-coding the value for a sleep while it looped waiting for the job status to be updated. It now respects the configured sleep value as set per the wait attribute in the initializer of QuantumInstance.

  • Fixed an issue with the schedule function where callers specifying a list of QuantumCircuit objects with a single entry would incorrectly be returned a single Schedule object instead of a list.

  • Fixed an issue with the plot_error_map visualization function which prevented it from working when run with a backend that had readout error defined in the provided backend’s BackendProperties or when running with a BackendV2 backend. Fixed #7879.

  • Fixed a bug that could result in exponential runtime and nontermination when a Pauli instance is given to method init_observables().

  • Fixed SabreSwap, and by extension transpile() with optimization_level=3, occasionally re-ordering measurements invalidly. Previously, if two measurements wrote to the same classical bit, SabreSwap could (depending on the coupling map) re-order them to produce a non-equivalent circuit. This behaviour was stochastic, so may not have appeared reliably. Fixed #7950

  • The SabreSwap transpiler pass, and by extension SabreLayout and transpile() at optimization_level=3, now has an escape mechanism to guarantee that it can never get stuck in an infinite loop. Certain inputs previously could, with a great amount of bad luck, get stuck in a stable local minimum of the search space and the pass would never make further progress. It will now force a series of swaps that allow the routing to continue if it detects it has not made progress recently. Fixed #7707.

  • Fixed an issue with QPY deserialization via the qpy.load() function of the UCRXGate, UCRYGate, and UCRZGate classes. Previously, a QPY file that contained any of these gates would error when trying to load the file. Fixed #7847.

Aer 0.10.4

No change

Ignis 0.7.0

No change

IBM Q Provider 0.19.1

0.19.1

Bug Fixes
  • 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.

Qiskit 0.36.0

Terra 0.20.0

No change

Aer 0.10.4

Upgrade Notes
  • Qiskit Aer is no longer compiled with unsafe floating-point optimisations. While most of the effects should have been localised to Qiskit Aer, some aspects of subnormal handling may previously have been leaked into user code by the library incorrectly setting the 「flush to zero」 mode. This will not happen any more.

Bug Fixes
  • Fix cache blocking transpiler to recognize superop to be cache blocked. This is fix for issue 1479 <https://github.com/Qiskit/qiskit-aer/issues/1479> now density_matrix with noise models can be parallelized. New test, test_noise.TestNoise.test_kraus_gate_noise_on_QFT_cache_blocking is added to verify this issue. Also this fix include fix for issue 1483 <https://github.com/Qiskit/qiskit-aer/issues/1483> discovered by adding new test case. This fixes measure over chunks for statevector.

  • 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.

  • device=Thrust was very slow for small number of qubits because OpenMP threading was always applied. This fix applies OpenMP threads as same as device=CPU by using statevector_parallel_threshold.

  • Qiskit Aer will no longer set the floating-point mode to 「flush to zero」 when loaded. Downstream users may previously have seen warnings from Numpy such as:

    The value of the smallest subnormal for <class 『numpy.float64』> type is zero.

    These will now no longer be emitted, and the floating-point handling will be correct.

  • Fixed a potential issue with running simulations on circuits that have the QuantumCircuit.metadata attribute set. The metadata attribute can be any python dictionary and previously qiskit-aer would attempt to JSON serialize the contents of the attribute to process it with the rest of the rest of the circuit input, even if the contents were not JSON serializable. This no longer occurs as the QuantumCircuit.metadata attribute is not used to run the simulation so now the contents are no serialized and instead are directly attached to the qiskit.result.Result object without attempting to JSON serialize the contents. Fixed #1435

Ignis 0.7.0

No change

IBM Q Provider 0.19.0

New Features
  • The qiskit-ibmq-provider package now supports IBM Quantum LiveData features. These features allow users to observe the real-time behavior of IBM Quantum backends while executing jobs. Specifically, the provider now includes a new tab in the backend Jupyter-related widget and supports the execution of jobs (via qiskit.providers.ibmq.IBMQBackend.run() method) with the live_data_enabled=True parameter in allowed IBM Quantum backends.

  • You can now specify a different logging level in the options keyword when submitting a Qiskit Runtime job with the qiskit.providers.ibmq.runtime.IBMRuntimeService.run() method.

Upgrade Notes
  • Python 3.6 support has been dropped since it has reached end of life in Dec 2021.

  • qiskit.providers.ibmq.random, the random number service which was used to access the CQC randomness extractor is no longer supported and has been removed.

Deprecation Notes
Bug Fixes

Qiskit 0.35.0

Terra 0.20.0

Prelude

The Qiskit Terra 0.20.0 release highlights are:

  • The introduction of multithreaded modules written in Rust to accelerate the performance of certain portions of Qiskit Terra and improve scaling with larger numbers of qubits. However, when building Qiskit from source a Rust compiler is now required.

  • More native support for working with a Target in the transpiler. Several passes now support working directly with a Target object which makes the transpiler robust in the types of backends it can target.

  • The introduction of the qiskit.primitives module. These APIs provide different abstraction levels for computing outputs of interest from QuantumCircuit and using backends. For example, the BaseEstimator defines an abstract interface for estimating an expectation value of an observable. This can then be used to construct higher level algorithms and applications that are built using the estimation of expectation values without having to worry about the implementation of computing the expectation value. This decoupling allows the implementation to improve in speed and quality while adhering to the defined abstract interface. Likewise, the BaseSampler computes quasi-probability distributions from circuit measurements. Other primitives will be introduced in the future.

This release no longer has support for Python 3.6. With this release, Python 3.7 through Python 3.10 are required.

New Features
  • Added a new constructor method for the Operator class, Operator.from_circuit() for creating a new Operator object from a QuantumCircuit. While this was possible normally using the default constructor, the Operator.from_circuit() method provides additional options to adjust how the operator is created. Primarily this lets you permute the qubit order based on a set Layout. For, example:

    from qiskit.circuit import QuantumCircuit
    from qiskit import transpile
    from qiskit.transpiler import CouplingMap
    from qiskit.quantum_info import Operator
    
    circuit = QuantumCircuit(3)
    circuit.h(0)
    circuit.cx(0, 1)
    circuit.cx(1, 2)
    
    cmap = CouplingMap.from_line(3)
    out_circuit = transpile(circuit, initial_layout=[2, 1, 0], coupling_map=cmap)
    operator = Operator.from_circuit(out_circuit)
    

    the operator variable will have the qubits permuted based on the layout so that it is identical to what is returned by Operator(circuit) before transpilation.

  • Added a new method DAGCircuit.copy_empty_like() to the DAGCircuit class. This method is used to create a new copy of an existing DAGCircuit object with the same structure but empty of any instructions. This method is the same as the private method _copy_circuit_metadata(), but instead is now part of the public API of the class.

  • The fake backend and fake provider classes which were previously available in qiskit.test.mock are now also accessible in a new module: qiskit.providers.fake_provider. This new module supersedes the previous module qiskit.test.mock which will be deprecated in Qiskit 0.21.0.

  • Added a new gate class, LinearFunction, that efficiently encodes a linear function (i.e. a function that can be represented by a sequence of CXGate and SwapGate gates).

  • FlowController classes (such as ConditionalController) can now be nested inside a PassManager instance when using the PassManager.append() method. This enables the use of nested logic to control the execution of passes in the PassManager. For example:

    from qiskit.transpiler import ConditionalController, PassManager
    from qiskit.transpiler.passes import (
      BasisTranslator, GatesInBasis, Optimize1qGatesDecomposition, FixedPoint, Depth
    )
    from qiskit.circuit.equivalence_library import SessionEquivalenceLibrary as sel
    
    pm = PassManager()
    
    def opt_control(property_set):
        return not property_set["depth_fixed_point"]
    
    def unroll_condition(property_set):
        return not property_set["all_gates_in_basis"]
    
    depth_check = [Depth(), FixedPoint("depth")]
    opt = [Optimize1qGatesDecomposition(['rx', 'ry', 'rz', 'rxx'])]
    unroll = [BasisTranslator(sel, ['rx', 'ry', 'rz', 'rxx'])]
    unroll_check = [GatesInBasis(['rx', 'ry', 'rz', 'rxx'])]
    flow_unroll = [ConditionalController(unroll, condition=unroll_condition)]
    
    pm.append(depth_check + opt + unroll_check + flow_unroll, do_while=opt_control)
    

    The pm PassManager object will only execute the BasisTranslator pass (in the unroll step) in each loop iteration if the unroll_condition is met.

  • The constructors for the ZFeatureMap and ZZFeatureMap classes have a new keyword argument parameter_prefix. This new argument is used to set the prefix of parameters of the data encoding circuit. For example:

    from qiskit.circuit.library import ZFeatureMap
    
    feature_map = ZFeatureMap(feature_dimension=4, parameter_prefix="my_prefix")
    feature_map.decompose().draw('mpl')
    
    _images/release_notes_6_0.png

    the generated ZFeatureMap circuit has prefixed all its internal parameters with the prefix "my_prefix".

  • The TemplateOptimization transpiler pass can now work with Gate objects that have ParameterExpression parameters. An illustrative example of using Parameters with TemplateOptimization is the following:

    from qiskit import QuantumCircuit, transpile, schedule
    from qiskit.circuit import Parameter
    
    from qiskit.transpiler import PassManager
    from qiskit.transpiler.passes import TemplateOptimization
    
    # New contributions to the template optimization
    from qiskit.transpiler.passes.calibration import RZXCalibrationBuilder, rzx_templates
    
    from qiskit.test.mock import FakeCasablanca
    backend = FakeCasablanca()
    
    phi = Parameter('φ')
    
    qc = QuantumCircuit(2)
    qc.cx(0,1)
    qc.p(2*phi, 1)
    qc.cx(0,1)
    print('Original circuit:')
    print(qc)
    
    pass_ = TemplateOptimization(**rzx_templates.rzx_templates(['zz2']))
    qc_cz = PassManager(pass_).run(qc)
    print('ZX based circuit:')
    print(qc_cz)
    
    # Add the calibrations
    pass_ = RZXCalibrationBuilder(backend)
    cal_qc = PassManager(pass_).run(qc_cz.bind_parameters({phi: 0.12}))
    
    # Transpile to the backend basis gates
    cal_qct = transpile(cal_qc, backend)
    qct = transpile(qc.bind_parameters({phi: 0.12}), backend)
    
    # Compare the schedule durations
    print('Duration of schedule with the calibration:')
    print(schedule(cal_qct, backend).duration)
    print('Duration of standard with two CNOT gates:')
    print(schedule(qct, backend).duration)
    

    outputs

    Original circuit:
    
    q_0: ──■──────────────■──
         ┌─┴─┐┌────────┐┌─┴─┐
    q_1: ┤ X ├┤ P(2*φ) ├┤ X ├
         └───┘└────────┘└───┘
    ZX based circuit:
                                             ┌─────────────┐            »
    q_0: ────────────────────────────────────┤0            ├────────────»
         ┌──────────┐┌──────────┐┌──────────┐│  Rzx(2.0*φ) │┌──────────┐»
    q_1: ┤ Rz(-π/2) ├┤ Rx(-π/2) ├┤ Rz(-π/2) ├┤1            ├┤ Rx(-2*φ) ├»
         └──────────┘└──────────┘└──────────┘└─────────────┘└──────────┘»
    «
    «q_0: ────────────────────────────────────────────────
    «     ┌──────────┐┌──────────┐┌──────────┐┌──────────┐
    «q_1: ┤ Rz(-π/2) ├┤ Rx(-π/2) ├┤ Rz(-π/2) ├┤ P(2.0*φ) ├
    «     └──────────┘└──────────┘└──────────┘└──────────┘
    Duration of schedule with the calibration:
    1600
    Duration of standard with two CNOT gates:
    6848
    
  • The DAGOpNode, DAGInNode and DAGOutNode classes now define a custom __repr__ method which outputs a representation. Per the Python documentation the output is a string representation that is roughly equivalent to the Python string used to create an equivalent object.

  • The performance of the SparsePauliOp.simplify() method has greatly improved by replacing the use of numpy.unique to compute unique elements of an array by a new similar function implemented in Rust that doesn’t pre-sort the array.

  • Added a new method equiv() to the SparsePauliOp class for testing the equivalence of a SparsePauliOp with another SparsePauliOp object. Unlike the == operator which compares operators element-wise, equiv() compares whether two operators are equivalent or not. For example:

    op = SparsePauliOp.from_list([("X", 1), ("Y", 1)])
    op2 = SparsePauliOp.from_list([("X", 1), ("Y", 1), ("Z", 0)])
    op3 = SparsePauliOp.from_list([("Y", 1), ("X", 1)])
    
    print(op == op2)  # False
    print(op == op3)  # False
    print(op.equiv(op2))  # True
    print(op.equiv(op3))  # True
    
  • Added new fake backend classes from snapshots of the IBM Quantum systems based on the BackendV2 interface and provided a Target for each backend. BackendV2 based versions of all the existing backends are added except for three old backends FakeRueschlikon, FakeTenerife and FakeTokyo as they do not have snapshots files available which are required for creating a new fake backend class based on BackendV2.

    These new V2 fake backends will enable testing and development of new features introduced by BackendV2 and Target such as improving the transpiler.

  • Added a new gate class XXMinusYYGate to the circuit library (qiskit.circuit.library) for the XX-YY interaction. This gate can be used to implement the bSwap gate and its powers. It also arises in the simulation of superconducting fermionic models.

  • The FakeBogota, FakeManila, FakeRome, and FakeSantiago fake backends which can be found in the qiskit.providers.fake_provider module can now be used as backends in Pulse experiments as they now include a PulseDefaults created from a snapshot of the equivalent IBM Quantum machine’s properties.

  • The ConsolidateBlocks pass has a new keyword argument on its constructor, target. This argument is used to specify a Target object representing the compilation target for the pass. If it is specified it supersedes the basis_gates kwarg. If a target is specified, the pass will respect the gates and qubits for the instructions defined in the Target when deciding which gates to consolidate into a unitary.

  • The Target class has a new method, instruction_supported() which is used to query the target to see if an instruction (the combination of an operation and the qubit(s) it is executed on) is supported on the backend modelled by the Target.

  • Added a new kwarg, metadata_serializer, to the qpy.dump() function for specifying a custom JSONEncoder subclass for use when serializing the QuantumCircuit.metadata attribute and a dual kwarg metadata_deserializer to the qpy.load() function for specifying a JSONDecoder subclass. By default the dump() and load() functions will attempt to JSON serialize and deserialize with the stdlib default json encoder and decoder. Since QuantumCircuit.metadata can contain any Python dictionary, even those with contents not JSON serializable by the default encoder, will lead to circuits that can’t be serialized. The new metadata_serializer argument for dump() enables users to specify a custom JSONEncoder that will be used with the internal json.dump() call for serializing the QuantumCircuit.metadata dictionary. This can then be paired with the new metadata_deserializer argument of the qpy.load() function to decode those custom JSON encodings. If metadata_serializer is specified on dump() but metadata_deserializer is not specified on load() calls the QPY will be loaded, but the circuit metadata may not be reconstructed fully.

    For example if you wanted to define a custom serialization for metadata and then load it you can do something like:

    from qiskit.qpy import dump, load
    from qiskit.circuit import QuantumCircuit, Parameter
    import json
    import io
    
    class CustomObject:
        """Custom string container object."""
    
        def __init__(self, string):
            self.string = string
    
        def __eq__(self, other):
            return self.string == other.string
    
    class CustomSerializer(json.JSONEncoder):
        """Custom json encoder to handle CustomObject."""
    
        def default(self, o):
            if isinstance(o, CustomObject):
                return {"__type__": "Custom", "value": o.string}
            return json.JSONEncoder.default(self, o)
    
    class CustomDeserializer(json.JSONDecoder):
        """Custom json decoder to handle CustomObject."""
    
        def __init__(self, *args, **kwargs):
            super().__init__(*args, object_hook=self.object_hook, **kwargs)
    
        def object_hook(self, o):
            """Hook to override default decoder."""
            if "__type__" in o:
                obj_type = o["__type__"]
                if obj_type == "Custom":
                    return CustomObject(o["value"])
            return o
    
    theta = Parameter("theta")
    qc = QuantumCircuit(2, global_phase=theta)
    qc.h(0)
    qc.cx(0, 1)
    qc.measure_all()
    circuits = [qc, qc.copy()]
    circuits[0].metadata = {"key": CustomObject("Circuit 1")}
    circuits[1].metadata = {"key": CustomObject("Circuit 2")}
    with io.BytesIO() as qpy_buf:
        dump(circuits, qpy_buf, metadata_serializer=CustomSerializer)
        qpy_buf.seek(0)
        new_circuits = load(qpy_buf, metadata_deserializer=CustomDeserializer)
    
  • The DenseLayout pass has a new keyword argument on its constructor, target. This argument is used to specify a Target object representing the compilation target for the pass. If it is specified it supersedes the other arguments on the constructor, coupling_map and backend_prop.

  • The Target class has a new method, operation_names_for_qargs(). This method is used to get the operation names (i.e. lookup key in the target) for the operations on a given qargs tuple.

  • A new pass DynamicalDecouplingPadding has been added to the qiskit.transpiler.passes module. This new pass supersedes the existing DynamicalDecoupling pass to work with the new scheduling workflow in the transpiler. It is a subclass of the BasePadding pass and depends on having scheduling and alignment analysis passes run prior to it in a PassManager. This new pass can take a pulse_alignment argument which represents a hardware constraint for waveform start timing. The spacing between gates comprising a dynamical decoupling sequence is now adjusted to satisfy this constraint so that the circuit can be executed on hardware with the constraint. This value is usually found in BackendConfiguration.timing_constraints. Additionally the pass also has an extra_slack_distribution option has been to control how to distribute the extra slack when the duration of the created dynamical decoupling sequence is shorter than the idle time of your circuit that you want to fill with the sequence. This defaults to middle which is identical to conventional behavior. The new strategy split_edges evenly divide the extra slack into the beginning and end of the sequence, rather than adding it to the interval in the middle of the sequence. This might result in better noise cancellation especially when pulse_alignment > 1.

  • The Z2Symmetries class now exposes the threshold tolerances used to chop small real and imaginary parts of coefficients. With this one can control how the coefficients of the tapered operator are simplified. For example:

    from qiskit.opflow import Z2Symmetries
    from qiskit.quantum_info import Pauli
    
    z2_symmetries = Z2Symmetries(
        symmetries=[Pauli("IIZI"), Pauli("IZIZ"), Pauli("ZIII")],
        sq_paulis=[Pauli("IIXI"), Pauli("IIIX"), Pauli("XIII")],
        sq_list=[1, 0, 3],
        tapering_values=[1, -1, -1],
        tol=1e-10,
    )
    

    By default, coefficients are chopped with a tolerance of tol=1e-14.

  • Added a chop() method to the SparsePauliOp class that truncates real and imaginary parts of coefficients individually. This is different from the SparsePauliOp.simplify() method which removes a coefficient only if the absolute value is close to 0. For example:

    >>> from qiskit.quantum_info import SparsePauliOp
    >>> op = SparsePauliOp(["X", "Y", "Z"], coeffs=[1+1e-17j, 1e-17+1j, 1e-17])
    >>> op.simplify()
    SparsePauliOp(['X', 'Y'],
                  coeffs=[1.e+00+1.e-17j, 1.e-17+1.e+00j])
    >>> op.chop()
    SparsePauliOp(['X', 'Y'],
                  coeffs=[1.+0.j, 0.+1.j])
    

    Note that the chop method does not accumulate the coefficents of the same Paulis, e.g.

    >>> op = SparsePauliOp(["X", "X"], coeffs=[1+1e-17j, 1e-17+1j)
    >>> op.chop()
    SparsePauliOp(['X', 'X'],
                  coeffs=[1.+0.j, 0.+1.j])
    
  • Added a new kwarg, target, to the constructor for the GatesInBasis transpiler pass. This new argument can be used to optionally specify a Target object that represents the backend. When set this Target will be used for determining whether a DAGCircuit contains gates outside the basis set and the basis_gates argument will not be used.

  • 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-terra with just a working Python environment will not be sufficient to install Qiskit. 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 here.

  • The Gradient and QFI classes can now calculate the imaginary part of expectation value gradients. When using a different measurement basis, i.e. -Y instead of Z, we can measure the imaginary part of gradients The measurement basis can be set with the aux_meas_op argument.

    For the gradients, aux_meas_op = Z computes 0.5Re[(⟨ψ(ω)|)O(θ)|dωψ(ω)〉] and aux_meas_op = -Y computes 0.5Im[(⟨ψ(ω)|)O(θ)|dωψ(ω)〉]. For the QFIs, aux_meas_op = Z computes 4Re[(dω⟨<ψ(ω)|)(dω|ψ(ω)〉)] and aux_meas_op = -Y computes 4Im[(dω⟨<ψ(ω)|)(dω|ψ(ω)〉)]. For example:

    from qiskit import QuantumRegister, QuantumCircuit
    from qiskit.opflow import CircuitStateFn, Y
    from qiskit.opflow.gradients.circuit_gradients import LinComb
    from qiskit.circuit import Parameter
    
    a = Parameter("a")
    b = Parameter("b")
    params = [a, b]
    
    q = QuantumRegister(1)
    qc = QuantumCircuit(q)
    qc.h(q)
    qc.rz(params[0], q[0])
    qc.rx(params[1], q[0])
    op = CircuitStateFn(primitive=qc, coeff=1.0)
    
    aux_meas_op = -Y
    
    prob_grad = LinComb(aux_meas_op=aux_meas_op).convert(operator=op, params=params)
    
  • The InstructionDurations class now has support for working with parameters of an instruction. Each entry in an InstructionDurations object now consists of a tuple of (inst_name, qubits, duration, parameters, unit). This enables an InstructionDurations to define durations for an instruction given a certain parameter value to account for different durations with different parameter values on an instruction that takes a numeric parameter.

  • Added a new value for the style keyword argument on the circuit drawer function circuit_drawer() and QuantumCircuit.draw() method, iqx_dark. When style is set to iqx_dark with the mpl drawer backend, the output visualization will use a color scheme similar to the the dark mode color scheme used by the IBM Quantum composer. For example:

    from qiskit.circuit import QuantumCircuit
    from matplotlib.pyplot import show
    
    circuit = QuantumCircuit(2)
    circuit.h(0)
    circuit.cx(0, 1)
    circuit.p(0.2, 1)
    
    circuit.draw("mpl", style="iqx-dark")
    
    _images/release_notes_7_0.png
  • Several lazy dependency checkers have been added to the new module qiskit.utils.optionals, which can be used to query if certain Qiskit functionality is available. For example, you can ask if Qiskit has detected the presence of matplotlib by asking if qiskit.utils.optionals.HAS_MATPLOTLIB. These objects only attempt to import their dependencies when they are queried, so you can use them in runtime code without affecting import time.

  • Import time for qiskit has been significantly improved, especially for those with many of Qiskit Terra’s optional dependencies installed.

  • The marginal_counts() function now supports marginalizing the memory field of an input Result object. For example, if the input result argument is a qiskit Result object obtained from a 4-qubit measurement we can marginalize onto the first qubit with:

    print(result.results[0].data.memory)
    marginal_result = marginal_counts(result, [0])
    print(marginal_result.results[0].data.memory)
    

    The output is:

    ['0x0', '0x1', '0x2', '0x3', '0x4', '0x5', '0x6', '0x7']
    ['0x0', '0x1', '0x0', '0x1', '0x0', '0x1', '0x0', '0x1']
    
  • The internals of the StochasticSwap algorithm have been reimplemented to be multithreaded and are now written in the Rust programming language instead of Cython. This significantly increases the run time performance of the compiler pass and by extension transpile() when run with optimization_level 0, 1, and 2. By default the pass will use up to the number of logical CPUs on your local system but you can control the number of threads used by the pass by setting the RAYON_NUM_THREADS environment variable to an integer value. For example, setting RAYON_NUM_THREADS=4 will run the StochasticSwap with 4 threads.

  • A new environment variable QISKIT_FORCE_THREADS is available for users to directly control whether potentially multithreaded portions of Qiskit’s code will run in multiple threads. Currently this is only used by the StochasticSwap transpiler pass but it likely will be used other parts of Qiskit in the future. When this env variable is set to TRUE any multithreaded code in Qiskit Terra will always use multiple threads regardless of any other runtime conditions that might have otherwise caused the function to use a single threaded variant. For example, in StochasticSwap if the pass is being run as part of a transpile() call with > 1 circuit that is being executed in parallel with multiprocessing via parallel_map() the StochasticSwap will not use multiple threads to avoid potentially oversubscribing CPU resources. However, if you’d like to use multiple threads in the pass along with multiple processes you can set QISKIT_FORCE_THREADS=TRUE.

  • New fake backend classes are available under qiskit.providers.fake_provider. These include mocked versions of ibm_cairo, ibm_hanoi, ibmq_kolkata, ibm_nairobi, and ibm_washington. As with the other fake backends, these include snapshots of calibration and error data taken from the real system, and can be used for local testing, compilation and simulation.

  • Introduced a new class StatePreparation. This class allows users to prepare a desired state in the same fashion as Initialize without the reset being automatically applied.

    For example, to prepare a qubit in the state \((|0\rangle - |1\rangle) / \sqrt{2}\):

    import numpy as np
    from qiskit import QuantumCircuit
    
    circuit = QuantumCircuit(1)
    circuit.prepare_state([1/np.sqrt(2), -1/np.sqrt(2)], 0)
    circuit.draw()
    

    The output is as:

         ┌─────────────────────────────────────┐
    q_0: ┤ State Preparation(0.70711,-0.70711) ├
         └─────────────────────────────────────┘
    
  • The Optimize1qGates transpiler pass now has support for optimizing U1Gate, U2Gate, and PhaseGate gates with unbound parameters in a circuit. Previously, if these gates had unbound parameters the pass would not use them. For example:

    from qiskit import QuantumCircuit
    from qiskit.circuit import Parameter
    from qiskit.transpiler import PassManager
    from qiskit.transpiler.passes import Optimize1qGates, Unroller
    
    phi = Parameter('φ')
    alpha = Parameter('α')
    
    qc = QuantumCircuit(1)
    qc.u1(2*phi, 0)
    qc.u1(alpha, 0)
    qc.u1(0.1, 0)
    qc.u1(0.2, 0)
    
    pm = PassManager([Unroller(['u1', 'cx']), Optimize1qGates()])
    nqc = pm.run(qc)
    

    will be combined to the circuit with only one single-qubit gate:

    qc = QuantumCircuit(1)
    qc.u1(2*phi + alpha + 0.3, 0)
    
  • The methods Pauli.evolve() and PauliList.evolve() now have a new keyword argument, frame, which is used to perform an evolution of a Pauli by a Clifford. If frame='h' (default) then it does the Heisenberg picture evolution of a Pauli by a Clifford (\(P' = C^\dagger P C\)), and if frame='s' then it does the Schrödinger picture evolution of a Pauli by a Clifford (\(P' = C P C^\dagger\)). The latter option yields a faster calculation, and is also useful in certain cases. This new option makes the calculation of the greedy Clifford decomposition method in decompose_clifford significantly faster.

  • Added a new module to Qiskit: qiskit.primitives. The primitives module is where APIs are defined which provide different abstractions around computing certain common functions from QuantumCircuit which abstracts away the details of the underlying execution on a Backend. This enables higher level algorithms and applications to concentrate on performing the computation and not need to worry about the execution and processing of results and have a standardized interface for common computations. For example, estimating an expectation value of a quantum circuit and observable can be performed by any class implementing the BaseEstimator class and consumed in a standardized manner regardless of the underlying implementation. Applications can then be written using the primitive interface directly.

    To start the module contains two types of primitives, the Sampler (see BaseSampler for the abstract class definition) and Estimator (see BaseEstimator for the abstract class definition). Reference implementations are included in the qiskit.primitives module and are built using the qiskit.quantum_info module which perform ideal simulation of primitive operation. The expectation is that provider packages will offer their own implementations of these interfaces for providers which can efficiently implement the protocol natively (typically using a classical runtime). Additionally, in the future for providers which do not offer a native implementation of the primitives a method will be provided which will enable constructing primitive objects from a Backend.

  • Added a new module, qiskit.qpy, which contains the functionality previously exposed in qiskit.circuit.qpy_serialization. The public functions previously exposed at qiskit.circuit.qpy_serialization, dump() and load() are now available from this new module (although they are still accessible from qiskit.circuit.qpy_serialization but this will be deprecated in a future release). This new module was added in the interest of the future direction of the QPY file format, which in future versions will support representing pulse Schedule and ScheduleBlock objects in addition to the QuantumCircuit objects it supports today.

  • The basis search strategy in BasisTranslator transpiler pass has been modified into a variant of Dijkstra search which greatly improves the runtime performance of the pass when attempting to target an unreachable basis.

  • The DenseLayout transpiler pass is now multithreaded, which greatly improves the runtime performance of the pass. By default, it will use the number of logical CPUs on your local system, but you can control the number of threads used by the pass by setting the RAYON_NUM_THREADS environment variable to an integer value. For example, setting RAYON_NUM_THREADS=4 will run the DenseLayout pass with 4 threads.

  • The internal computations of Statevector.expectation_value() and DensityMatrix.expectation_value() methods have been reimplemented in the Rust programming language. This new implementation is multithreaded and by default for a Statevector or DensityMatrix >= 19 qubits will spawn a thread pool with the number of logical CPUs available on the local system. You can you can control the number of threads used by setting the RAYON_NUM_THREADS environment variable to an integer value. For example, setting RAYON_NUM_THREADS=4 will only use 4 threads in the thread pool.

  • Added a new SparsePauliOp.from_sparse_list() constructor that takes an iterable, where the elements represent Pauli terms that are themselves sparse, so that "XIIIIIIIIIIIIIIIX" can now be written as ("XX", [0, 16]). For example, the operator

    \[H = X_0 Z_3 + 2 Y_1 Y_4\]

    can now be constructed as

    op = SparsePauliOp.from_sparse_list([("XZ", [0, 3], 1), ("YY", [1, 4], 2)], num_qubits=5)
    # or equivalently, as previously
    op = SparsePauliOp.from_list([("IZIIX", 1), ("YIIYI", 2)])
    

    This facilitates the construction of very sparse operators on many qubits, as is often the case for Ising Hamiltonians.

  • The UnitarySynthesis transpiler pass has a new keyword argument on its constructor, target. This can be used to optionally specify a Target object which represents the compilation target for the pass. When it’s specified it will supersede the values set for basis_gates, coupling_map, and backend_props.

  • The UnitarySynthesisPlugin abstract plugin class has a new optional attribute implementations can add, supports_target. If a plugin has this attribute set to True a Target object will be passed in the options payload under the target field. The expectation is that this Target object will be used in place of coupling_map, gate_lengths, basis_gates, and gate_errors.

  • Introduced a new transpiler pass workflow for building PassManager objects for scheduling QuantumCircuit objects in the transpiler. In the new workflow scheduling and alignment passes are all AnalysisPass objects that only update the property set of the pass manager, specifically new property set item node_start_time, which holds the absolute start time of each opnode. A separate TransformationPass such as PadDelay is subsequently used to apply scheduling to the DAG. This new workflow is both more efficient and can correct for additional timing constraints exposed by a backend.

    Previously, the pass chain would have been implemented as scheduling -> alignment which were both transform passes thus there were multiple DAGCircuit instances recreated during each pass. In addition, scheduling occured in each pass to obtain instruction start time. Now the required pass chain becomes scheduling -> alignment -> padding where the DAGCircuit update only occurs at the end with the padding pass.

    For those who are creating custom PassManager objects that involve circuit scheduling you will need to adjust your PassManager to insert one of the BasePadding passes (currently either PadDelay or PadDynamicalDecoupling can be used) at the end of the scheduling pass chain. Without the padding pass the scheduling passes will not be reflected in the output circuit of the run() method of your custom PassManager.

    For example, if you were previously building your PassManager with something like:

    from qiskit.transpiler import PassManager
    from qiskit.transpiler.passes import TimeUnitConversion, ALAPSchedule, ValidatePulseGates, AlignMeasures
    
    pm = PassManager()
    scheduling = [
        ALAPSchedule(instruction_durations), PadDelay()),
        ValidatePulseGates(granularity=timing_constraints.granularity, min_length=timing_constraints.min_length),
        AlignMeasures(alignment=timing_constraints.acquire_alignment),
    ]
    pm.append(scheduling)
    

    you can instead use:

    from qiskit.transpiler import PassManager
    from qiskit.transpiler.passes import TimeUnitConversion, ALAPScheduleAnalysis, ValidatePulseGates, AlignMeasures, PadDelay
    
    pm = PassManager()
    scheduling = [
        ALAPScheduleAnalysis(instruction_durations), PadDelay()),
        ConstrainedReschedule(acquire_alignment=timing_constraints.acquire_alignment, pulse_alignment=timing_constraints.pulse_alignment),
        ValidatePulseGates(granularity=timing_constraints.granularity, min_length=timing_constraints.min_length),
        PadDelay()
    ]
    pm.append(scheduling)
    

    which will both be more efficient and also align instructions based on any hardware constraints.

  • Added a new transpiler pass ConstrainedReschedule pass. The ConstrainedReschedule pass considers both hardware alignment constraints that can be definied in a BackendConfiguration object, pulse_alignment and acquire_alignment. This new class supersedes the previosuly existing AlignMeasures as it performs the same alignment (via the property set) for measurement instructions in addition to general instruction alignment. By setting the acquire_alignment constraint argument for the ConstrainedReschedule pass it is a drop-in replacement of AlignMeasures when paired with a new BasePadding pass.

  • Added two new transpiler passes ALAPScheduleAnalysis and ASAPScheduleAnalysis which superscede the ALAPSchedule and ASAPSchedule as part of the reworked transpiler workflow for schedling. The new passes perform the same scheduling but in the property set and relying on a BasePadding pass to adjust the circuit based on all the scheduling alignment analysis.

    The standard behavior of these passes also aligns timing ordering with the topological ordering of the DAG nodes. This change may affect the scheduling outcome if it includes conditional operations, or simultaneously measuring two qubits with the same classical register (edge-case). To reproduce conventional behavior, set clbit_write_latency identical to the measurement instruction length.

    For example, consider scheduling an input circuit like:

         ┌───┐┌─┐
    q_0: ┤ X ├┤M├──────────────
         └───┘└╥┘   ┌───┐
    q_1: ──────╫────┤ X ├──────
               ║    └─╥─┘   ┌─┐
    q_2: ──────╫──────╫─────┤M├
               ║ ┌────╨────┐└╥┘
    c: 1/══════╩═╡ c_0=0x1 ╞═╩═
               0 └─────────┘ 0
    
    from qiskit import QuantumCircuit
    from qiskit.transpiler import InstructionDurations, PassManager
    from qiskit.transpiler.passes import ALAPScheduleAnalysis, PadDelay, SetIOLatency
    from qiskit.visualization.timeline import draw
    
    circuit = QuantumCircuit(3, 1)
    circuit.x(0)
    circuit.measure(0, 0)
    circuit.x(1).c_if(0, 1)
    circuit.measure(2, 0)
    
    durations = InstructionDurations([("x", None, 160), ("measure", None, 800)])
    
    pm = PassManager(
        [
          SetIOLatency(clbit_write_latency=800, conditional_latency=0),
          ALAPScheduleAnalysis(durations),
          PadDelay(),
        ]
    )
    draw(pm.run(circuit))
    
    _images/release_notes_8_0.png

    As you can see in the timeline view, the measurement on q_2 starts before the conditional X gate on the q_1, which seems to be opposite to the topological ordering of the node. This is also expected behavior because clbit write-access happens at the end edge of the measure instruction, and the read-access of the conditional gate happens the begin edge of the instruction. Thus topological ordering is preserved on the timeslot of the classical register, which is not captured by the timeline view. However, this assumes a paticular microarchitecture design, and the circuit is not necessary scheduled like this.

    By using the default configuration of passes, the circuit is schedule like below.

    from qiskit import QuantumCircuit
    from qiskit.transpiler import InstructionDurations, PassManager
    from qiskit.transpiler.passes import ALAPScheduleAnalysis, PadDelay
    from qiskit.visualization.timeline import draw
    
    circuit = QuantumCircuit(3, 1)
    circuit.x(0)
    circuit.measure(0, 0)
    circuit.x(1).c_if(0, 1)
    circuit.measure(2, 0)
    
    durations = InstructionDurations([("x", None, 160), ("measure", None, 800)])
    
    pm = PassManager([ALAPScheduleAnalysis(durations), PadDelay()])
    draw(pm.run(circuit))
    
    _images/release_notes_9_0.png

    Note that clbit is locked throughout the measurement instruction interval. This behavior is designed based on the Qiskit Pulse, in which the acquire instruction takes AcquireChannel and MemorySlot which are not allowed to overlap with other instructions, i.e. simultaneous memory access from the different instructions is prohibited. This also always aligns the timing ordering with the topological node ordering.

  • Added a new transpiler pass PadDynamicalDecoupling which supersedes the DynamicalDecoupling pass as part of the reworked transpiler workflow for scheduling. This new pass will insert dynamical decoupling sequences into the circuit per any scheduling and alignment analysis that occured in earlier passes.

  • The plot_gate_map() visualization function and the functions built on top of it, plot_error_map() and plot_circuit_layout(), have a new keyword argument, qubit_coordinates. This argument takes a sequence of 2D coordinates to use for plotting each qubit in the backend being visualized. If specified this sequence must have a length equal to the number of qubits on the backend and it will be used instead of the default behavior.

  • The plot_gate_map() visualization function and the functions built on top of it, plot_error_map() and plot_circuit_layout(), now are able to plot any backend not just those with the number of qubits equal to one of the IBM backends. This relies on the retworkx spring_layout() function to generate the layout for the visualization. If the default layout doesn’t work with a backend’s particular coupling graph you can use the qubit_coordinates function to set a custom layout.

  • Added a new transpiler pass, SetIOLatency. This pass takes two arguments clbit_write_latency and conditional_latency to define the I/O latency for classical bits and classical conditions on a backend. This pass will then define these values on the pass manager’s property set to enable subsequent scheduling and alignment passes to correct for these latencies and provide a more presice scheduling output of a dynamic circuit.

  • A new transpiler pass PadDelay has been added. This pass fills idle time on the qubit wires with Delay instructions. This pass is part of the new workflow for scheduling passes in the transpiler and depends on a scheduling analysis pass (such as ALAPScheduleAnalysis or ASAPScheduleAnalysis) and any alignment passes (such as ConstrainedReschedule) to be run prior to PadDelay.

  • The VF2Layout transpiler pass has a new keyword argument, target which is used to provide a Target object for the pass. When specified, the Target will be used by the pass for all information about the target device. If it is specified, the target option will take priority over the coupling_map and properties arguments.

  • Allow callables as optimizers in VQE and QAOA. Now, the optimizer can either be one of Qiskit’s optimizers, such as SPSA or a callable with the following signature:

    from qiskit.algorithms.optimizers import OptimizerResult
    
    def my_optimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
        # Args:
        #     fun (callable): the function to minimize
        #     x0 (np.ndarray): the initial point for the optimization
        #     jac (callable, optional): the gradient of the objective function
        #     bounds (list, optional): a list of tuples specifying the parameter bounds
    
        result = OptimizerResult()
        result.x = # optimal parameters
        result.fun = # optimal function value
        return result
    

    The above signature also allows to directly pass any SciPy minimizer, for instance as

    from functools import partial
    from scipy.optimize import minimize
    
    optimizer = partial(minimize, method="L-BFGS-B")
    
Known Issues
  • When running parallel_map() (which is done internally by performance sensitive functions such as transpile() and assemble()) in a subprocess launched outside of parallel_map(), it is possible that the parallel dispatch performed inside parallel_map() will hang and never return. This is due to upstream issues in CPython around the default method to launch subprocesses on Linux and macOS with Python 3.7 (see https://bugs.python.org/issue40379 for more details). If you encounter this, you have two options: you can either remove the nested parallel processes, as calling parallel_map() from a main process should work fine; or you can manually call the CPython standard library multiprocessing module to perform similar parallel dispatch from a subprocess, but use the "spawn" or "forkserver" launch methods to avoid the potential to have things get stuck and never return.

Upgrade Notes
  • The classes Qubit, Clbit and AncillaQubit now have the __slots__ attribute. This is to reduce their memory usage. As a side effect, they can no longer have arbitrary data attached as attributes to them. This is very unlikely to have any effect on downstream code other than performance benefits.

  • The core dependency retworkx had its version requirement bumped to 0.11.0, up from 0.10.1. This improves the performance of transpilation pass ConsolidateBlocks.

  • The minimum supported version of symengine is now 0.9.0. This was necessary to improve compatibility with Python’s pickle module which is used internally as part of parallel dispatch with parallel_map().

  • The default value of QISKIT_PARALLEL when running with Python 3.9 on Linux is now set to TRUE. This means when running parallel_map() or functions that call it internally, such as transpile() and assemble(), the function will be executed in multiple processes and should have better run time performance. This change was made because the issues with reliability of parallel dispatch appear to have been resolved (see #6188 for more details). If you still encounter issues because of this you can disable multiprocessing and revert to the previous default behavior by setting the QISKIT_PARALLEL environment variable to FALSE, or setting the parallel option to False in your user config file (also please file an issue so we can track any issues related to multiprocessing).

  • The previously deprecated MSGate gate class previously found in qiskit.circuit.library has been removed. It was originally deprecated in the 0.16.0 release. Instead the GMS class should be used, as this allows you to create an equivalent 2 qubit MS gate in addition to an MSGate for any number of qubits.

  • The previously deprecated mirror() method of the Instruction class has been removed. It was originally deprecated in 0.15.0 release. Instead you should use Instruction.reverse_ops().

  • The previously deprecated angle argument on the constructors for the C3SXGate and C3XGate gate classes has been removed. It was originally deprecated in the 0.17.0 release. Instead for fractional 3-controlled X gates you can use the C3XGate.power() method.

  • Support for using np.ndarray objects as part of the params attribute of a Gate object has been removed. This has been deprecated since Qiskit Terra 0.16.0 and now will no longer work. Instead one should create a new subclass of Gate and explicitly allow a np.ndarray input by overloading the validate_parameter() method.

  • A new extra csp-layout-pass has been added to the install target for pip install qiskit-terra, and is also included in the all extra. This has no effect in Qiskit Terra 0.20, but starting from Qiskit Terra 0.21, the dependencies needed only for the CSPLayout transpiler pass will be downgraded from requirements to optionals, and installed by this extra. You can prepare a package that depends on this pass by setting its requirements (or pip install command) to target qiskit-terra[csp-layout-pass].

  • Support for running with Python 3.6 has been removed. To run Qiskit you need a minimum Python version of 3.7.

  • The AmplitudeEstimator now inherits from the ABC class from the Python standard library. This requires any subclass to implement the estimate() method when previously it wasn’t required. This was done because the original intent of the class was to always be a child class of ABC, as the estimate() is required for the operation of an AmplitudeEstimator object. However, if you were previously defining an AmplitudeEstimator subclass that didn’t implement estimate() this will now result in an error.

  • The error raised by HoareOptimizer if the optional dependency z3 is not available has changed from TranspilerError to MissingOptionalLibraryError (which is both a QiskitError and an ImportError). This was done to be consistent with the other optional dependencies.

  • On Linux, the minimum library support has been raised from the manylinux2010 VM to manylinux2014. This mirrors similar changes in Numpy and Scipy. There should be no meaningful effect for most users, unless your system still contains a very old version of glibc.

  • The marginal_counts() function when called with a Result object input, will now marginalize the memory field of experiment data if it’s set in the input Result. Previously, the memory field in the the input was not marginalized. This change was made because the previous behavior would result in the counts field not matching the memory field after marginal_counts() was called. If the previous behavior is desired it can be restored by setting marginalize_memory=None as an argument to marginal_counts() which will not marginalize the memory field.

  • The StochasticSwap transpiler pass may return different results with the same seed value set. This is due to the internal rewrite of the transpiler pass to improve runtime performance. However, this means that if you ran