# Release Notes¶

## Version History¶

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

Table 1 Version History

Qiskit Metapackage Version

qiskit-terra

qiskit-aer

qiskit-ignis

qiskit-ibmq-provider

qiskit-aqua

0.14.1

0.11.1

0.3.4

0.2.0

0.4.5

0.6.2

0.14.0

0.11.0

0.3.4

0.2.0

0.4.4

0.6.1

0.13.0

0.10.0

0.3.2

0.2.0

0.3.3

0.6.1

0.12.2

0.9.1

0.3.0

0.2.0

0.3.3

0.6.0

0.12.1

0.9.0

0.3.0

0.2.0

0.3.3

0.6.0

0.12.0

0.9.0

0.3.0

0.2.0

0.3.2

0.6.0

0.11.2

0.8.2

0.2.3

0.1.1

0.3.2

0.5.5

0.11.1

0.8.2

0.2.3

0.1.1

0.3.1

0.5.3

0.11.0

0.8.2

0.2.3

0.1.1

0.3.0

0.5.2

0.10.5

0.8.2

0.2.1

0.1.1

0.2.2

0.5.2

0.10.4

0.8.2

0.2.1

0.1.1

0.2.2

0.5.1

0.10.3

0.8.1

0.2.1

0.1.1

0.2.2

0.5.1

0.10.2

0.8.0

0.2.1

0.1.1

0.2.2

0.5.1

0.10.1

0.8.0

0.2.0

0.1.1

0.2.2

0.5.0

0.10.0

0.8.0

0.2.0

0.1.1

0.2.1

0.5.0

0.9.0

0.8.0

0.2.0

0.1.1

0.1.1

0.5.0

0.8.1

0.7.2

0.1.1

0.1.0

0.8.0

0.7.1

0.1.1

0.1.0

0.7.3

>=0.7,<0.8

>=0.1,<0.2

0.7.2

>=0.7,<0.8

>=0.1,<0.2

0.7.1

>=0.7,<0.8

>=0.1,<0.2

0.7.0

>=0.7,<0.8

>=0.1,<0.2

Note

For the 0.7.0, 0.7.1, and 0.7.2 meta-package releases the Versioning policy was not formalized yet.

## Notable Changes¶

### Qiskit 0.14.0¶

#### Terra 0.11.0¶

##### Prelude¶

The 0.11.0 release includes several new features and bug fixes. The biggest change for this release is the addition of the pulse scheduler. This allows users to define their quantum program as a QuantumCircuit and then map it to the underlying pulse instructions that will control the quantum hardware to implement the circuit.

##### New Features¶
• Added 5 new commands to easily retrieve user-specific data from BackendProperties: gate_property, gate_error, gate_length, qubit_property, t1, t2, readout_error and frequency. They return the specific values of backend properties. For example:

from qiskit.test.mock import FakeOurense
backend = FakeOurense()
properties = backend.properties()

gate_property = properties.gate_property('u1')
gate_error = properties.gate_error('u1', 0)
gate_length = properties.gate_length('u1', 0)
qubit_0_property = properties.qubit_property(0)
t1_time_0 = properties.t1(0)
t2_time_0 = properties.t2(0)
frequency_0 = properties.frequency(0)

• Added method Instruction.is_parameterized() to check if an instruction object is parameterized. This method returns True if and only if instruction has a ParameterExpression or Parameter object for one of its params.

• Added a new analysis pass Layout2qDistance. This pass allows to “score” a layout selection, once property_set['layout'] is set. The score will be the sum of distances for each two-qubit gate in the circuit, when they are not directly connected. This scoring does not consider direction in the coupling map. The lower the number, the better the layout selection is.

For example, consider a linear coupling map [0]--[2]--[1] and the following circuit:

qr = QuantumRegister(2, 'qr')
circuit = QuantumCircuit(qr)
circuit.cx(qr[0], qr[1])


If the layout is {qr[0]:0, qr[1]:1}, Layout2qDistance will set property_set['layout_score'] = 1. If the layout is {qr[0]:0, qr[1]:2}, then the result is property_set['layout_score'] = 0. The lower the score, the better.

• Added qiskit.QuantumCircuit.cnot as an alias for the cx method of QuantumCircuit. The names cnot and cx are often used interchangeably now the cx method can be called with either name.

• Added qiskit.QuantumCircuit.toffoli as an alias for the ccx method of QuantumCircuit. The names toffoli and ccx are often used interchangeably now the ccx method can be called with either name.

• Added qiskit.QuantumCircuit.fredkin as an alias for the cswap method of QuantumCircuit. The names fredkin and cswap are often used interchangeably now the cswap method can be called with either name.

• The latex output mode for qiskit.visualization.circuit_drawer() and the qiskit.circuit.QuantumCircuit.draw() method now has a mode to passthrough raw latex from gate labels and parameters. The syntax for doing this mirrors matplotlib’s mathtext mode syntax. Any portion of a label string between a pair of ‘$’ characters will be treated as raw latex and passed directly into the generated output latex. This can be leveraged to add more advanced formatting to circuit diagrams generated with the latex drawer. Prior to this release all gate labels were run through a utf8 -> latex conversion to make sure that the output latex would compile the string as expected. This is still what happens for all portions of a label outside the ‘$’ pair. Also if you want to use a dollar sign in your label make sure you escape it in the label string (ie '\$'). You can mix and match this passthrough with the utf8 -> latex conversion to create the exact label you want, for example: from qiskit import circuit circ = circuit.QuantumCircuit(2) circ.h([0, 1]) circ.append(circuit.Gate(name='α_gate', num_qubits=1, params=[0]), [0]) circ.append(circuit.Gate(name='α_gate$_2$', num_qubits=1, params=[0]), [1]) circ.append(circuit.Gate(name='\$α\$_gate', num_qubits=1, params=[0]), [1]) circ.draw(output='latex')  will now render the first custom gate’s label as α_gate, the second will be α_gate with a 2 subscript, and the last custom gate’s label will be $α\$_gate.

• Add ControlledGate class for representing controlled gates. Controlled gate instances are created with the control(n) method of Gate objects where n represents the number of controls. The control qubits come before the controlled qubits in the new gate. For example:

from qiskit import QuantumCircuit
from qiskit.extensions import HGate
hgate = HGate()
circ = QuantumCircuit(4)
circ.append(hgate.control(3), [0, 1, 2, 3])
print(circ)


generates:

q_0: |0>──■──
│
q_1: |0>──■──
│
q_2: |0>──■──
┌─┴─┐
q_3: |0>┤ H ├
└───┘

• Allowed values of meas_level parameters and fields can now be a member from the IntEnum class qiskit.qobj.utils.MeasLevel. This can be used when calling execute (or anywhere else meas_level is specified) with a pulse experiment. For example:

from qiskit import QuantumCircuit, transpile, schedule, execute
from qiskit.test.mock import FakeOpenPulse2Q
from qiskit.qobj.utils import MeasLevel, MeasReturnType

backend = FakeOpenPulse2Q()
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0,1)
qc_transpiled = transpile(qc, backend)
sched = schedule(qc_transpiled, backend)
execute(sched, backend, meas_level=MeasLevel.CLASSIFIED)


In this above example, meas_level=MeasLevel.CLASSIFIED and meas_level=2 can be used interchangably now.

• A new layout selector based on constraint solving is included. CSPLayout models the problem of finding a layout as a constraint problem and uses recursive backtracking to solve it.

cmap16 = CouplingMap(FakeRueschlikon().configuration().coupling_map)

qr = QuantumRegister(5, 'q')
circuit = QuantumCircuit(qr)
circuit.cx(qr[0], qr[1])
circuit.cx(qr[0], qr[2])
circuit.cx(qr[0], qr[3])

pm = PassManager(CSPLayout(cmap16))
circuit_after = pm.run(circuit)
print(pm.property_set['layout'])

Layout({
1: Qubit(QuantumRegister(5, 'q'), 1),
2: Qubit(QuantumRegister(5, 'q'), 0),
3: Qubit(QuantumRegister(5, 'q'), 3),
4: Qubit(QuantumRegister(5, 'q'), 4),
15: Qubit(QuantumRegister(5, 'q'), 2)
})


The parameter CSPLayout(...,strict_direction=True) is more restrictive but it will guarantee there is no need of running CXDirection after.

pm = PassManager(CSPLayout(cmap16, strict_direction=True))
circuit_after = pm.run(circuit)
print(pm.property_set['layout'])

Layout({
8: Qubit(QuantumRegister(5, 'q'), 4),
11: Qubit(QuantumRegister(5, 'q'), 3),
5: Qubit(QuantumRegister(5, 'q'), 1),
6: Qubit(QuantumRegister(5, 'q'), 0),
7: Qubit(QuantumRegister(5, 'q'), 2)
})


If the constraint system is not solvable, the layout property is not set.

circuit.cx(qr[0], qr[4])
pm = PassManager(CSPLayout(cmap16))
circuit_after = pm.run(circuit)
print(pm.property_set['layout'])

None

• PulseBackendConfiguration (accessed normally as backend.configuration()) has been extended with useful methods to explore its data and the functionality that exists in PulseChannelSpec. PulseChannelSpec will be deprecated in the future. For example:

backend = provider.get_backend(backend_name)
config = backend.configuration()
q0_drive = config.drive(0)  # or, DriveChannel(0)
q0_meas = config.measure(0)  # MeasureChannel(0)
q0_acquire = config.acquire(0)  # AcquireChannel(0)
config.hamiltonian  # Returns a dictionary with hamiltonian info
config.sample_rate()  # New method which returns 1 / dt

• PulseDefaults (accessed normally as backend.defaults()) has an attribute, circuit_instruction_map which has the methods of CmdDef. The new circuit_instruction_map is an InstructionScheduleMap object with three new functions beyond what CmdDef had:

• qubit_instructions(qubits) returns the operations defined for the qubits

• assert_has(instruction, qubits) raises an error if the op isn’t defined

• remove(instruction, qubits) like pop, but doesn’t require parameters

There are some differences from the CmdDef:

• __init__ takes no arguments

• cmds and cmd_qubits are deprecated and replaced with instructions and qubits_with_instruction

Example:

backend = provider.get_backend(backend_name)
inst_map = backend.defaults().circuit_instruction_map
qubit = inst_map.qubits_with_instruction('u3')[0]
x_gate = inst_map.get('u3', qubit, P0=np.pi, P1=0, P2=np.pi)
pulse_schedule = x_gate(DriveChannel(qubit))

• A new kwarg parameter, show_framechange_channels to optionally disable displaying channels with only framechange instructions in pulse visualizations was added to the qiskit.visualization.pulse_drawer() function and qiskit.pulse.Schedule.draw() method. When this new kwarg is set to False the output pulse schedule visualization will not include any channels that only include frame changes.

For example:

from qiskit.pulse import *
from qiskit.pulse import pulse_lib

gp0 = pulse_lib.gaussian(duration=20, amp=1.0, sigma=1.0)
sched = Schedule()
channel_a = DriveChannel(0)
channel_b = DriveChannel(1)
sched = sched.append(gp0(channel_a))
sched = sched.insert(60, FrameChange(phase=-1.57)(channel_a))
sched = sched.insert(0, PersistentValue(value=0.2 + 0.4j)(
channel_a))
sched = sched.insert(30, FrameChange(phase=-1.50)(channel_b))
sched = sched.insert(70, FrameChange(phase=1.50)(channel_b))

sched.draw(show_framechange_channels=False)

• A new utility function qiskit.result.marginal_counts() is added which allows marginalization of the counts over some indices of interest. This is useful when more qubits are measured than needed, and one wishes to get the observation counts for some subset of them only.

• When passmanager.run(...) is invoked with more than one circuit, the transpilation of these circuits will run in parallel.

• PassManagers can now be sliced to create a new PassManager containing a subset of passes using the square bracket operator. This allow running or drawing a portion of the PassManager for easier testing and visualization. For example let’s try to draw the first 3 passes of a PassManager pm, or run just the second pass on our circuit:

pm[0:4].draw()
circuit2 = pm[1].run(circuit)


Also now, PassManagers can be created by adding two PassManagers or by directly adding a pass/list of passes to a PassManager.

pm = pm1[0] + pm2[1:3]
pm += [setLayout, unroller]

• A basic scheduler module has now been added to Qiskit. The scheduler schedules an input transpiled QuantumCircuit into a pulse Schedule. The scheduler accepts as input a Schedule and either a pulse Backend, or a CmdDef which relates circuit Instruction objects on specific qubits to pulse Schedules and a meas_map which determines which measurements must occur together.

Scheduling example:

from qiskit import QuantumCircuit, transpile, schedule
from qiskit.test.mock import FakeOpenPulse2Q

backend = FakeOpenPulse2Q()
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0,1)
qc_transpiled = transpile(qc, backend)
schedule(qc_transpiled, backend)


The scheduler currently supports two scheduling policies, as_late_as_possible (alap) and as_soon_as_possible (asap), which respectively schedule pulse instructions to occur as late as possible or as soon as possible across qubits in a circuit. The scheduling policy may be selected with the input argument method, for example:

schedule(qc_transpiled, backend, method='alap')


It is easy to use a pulse Schedule within a QuantumCircuit by mapping it to a custom circuit instruction such as a gate which may be used in a QuantumCircuit. To do this, first, define the custom gate and then add an entry into the CmdDef for the gate, for each qubit that the gate will be applied to. The gate can then be used in the QuantumCircuit. At scheduling time the gate will be mapped to the underlying pulse schedule. Using this technique allows easy integration with preexisting qiskit modules such as Ignis.

For example:

from qiskit import pulse, circuit, schedule
from qiskit.pulse import pulse_lib

custom_cmd_def = pulse.CmdDef()

# create custom gate
custom_gate = circuit.Gate(name='custom_gate', num_qubits=1, params=[])

# define schedule for custom gate
custom_schedule = pulse.Schedule()
custom_schedule += pulse_lib.gaussian(20, 1.0, 10)(pulse.DriveChannel)

# add schedule to custom gate with same name

# use custom gate in a circuit
custom_qc = circuit.QuantumCircuit(1)
custom_qc.append(custom_gate, qargs=[0])

# schedule the custom gate
schedule(custom_qc, cmd_def=custom_cmd_def, meas_map=[[0]])

##### Known Issues¶
• The feature for transpiling in parallel when passmanager.run(...) is invoked with more than one circuit is not supported under Windows. See #2988 for more details.

• The qiskit.pulse.channels.SystemTopology class was used as a helper class for PulseChannelSpec. It has been removed since with the deprecation of PulseChannelSpec and changes to BackendConfiguration make it unnecessary.

• The previously deprecated representation of qubits and classical bits as tuple, which was deprecated in the 0.9 release, has been removed. The use of Qubit and Clbit objects is the new way to represent qubits and classical bits.

• The previously deprecated representation of the basis set as single string has been removed. A list of strings is the new preferred way.

• The method BaseModel.as_dict, which was deprecated in the 0.9 release, has been removed in favor of the method BaseModel.to_dict.

• In PulseDefaults (accessed normally as backend.defaults()), qubit_freq_est and meas_freq_est are now returned in Hz rather than GHz. This means the new return values are 1e9 * their previous value.

• dill was added as a requirement. This is needed to enable running passmanager.run() in parallel for more than one circuit.

• The previously deprecated gate UBase, which was deprecated in the 0.9 release, has been removed. The gate U3Gate should be used instead.

• The previously deprecated gate CXBase, which was deprecated in the 0.9 release, has been removed. The gate CnotGate should be used instead.

• The instruction snapshot used to implicitly convert the label parameter to string. That conversion has been removed and an error is raised if a string is not provided.

• The previously deprecated gate U0Gate, which was deprecated in the 0.9 release, has been removed. The gate IdGate should be used instead to insert delays.

##### Deprecation Notes¶
• The qiskit.pulse.CmdDef class has been deprecated. Instead you should use the qiskit.pulse.InstructionScheduleMap. The InstructionScheduleMap object for a pulse enabled system can be accessed at backend.defaults().instruction_schedules.

• PulseChannelSpec is being deprecated. Use BackendConfiguration instead. The backend configuration is accessed normally as backend.configuration(). The config has been extended with most of the functionality of PulseChannelSpec, with some modifications as follows, where 0 is an exemplary qubit index:

pulse_spec.drives[0]   -> config.drive(0)
pulse_spec.measures[0] -> config.measure(0)
pulse_spec.acquires[0] -> config.acquire(0)
pulse_spec.controls[0] -> config.control(0)


Now, if there is an attempt to get a channel for a qubit which does not exist for the device, a BackendConfigurationError will be raised with a helpful explanation.

The methods memoryslots and registerslots of the PulseChannelSpec have not been migrated to the backend configuration. These classical resources are not restrained by the physical configuration of a backend system. Please instantiate them directly:

pulse_spec.memoryslots[0] -> MemorySlot(0)
pulse_spec.registerslots[0] -> RegisterSlot(0)


The qubits method is not migrated to backend configuration. The result of qubits can be built as such:

[q for q in range(backend.configuration().n_qubits)]

• Qubit within pulse.channels has been deprecated. They should not be used. It is possible to obtain channel <=> qubit mappings through the BackendConfiguration (or backend.configuration()).

• The function qiskit.visualization.circuit_drawer.qx_color_scheme() has been deprecated. This function is no longer used internally and doesn’t reflect the current IBM QX style. If you were using this function to generate a style dict locally you must save the output from it and use that dictionary directly.

• The Exception TranspilerAccessError has been deprecated. An alternative function TranspilerError can be used instead to provide the same functionality. This alternative function provides the exact same functionality but with greater generality.

• Buffers in Pulse are deprecated. If a nonzero buffer is supplied, a warning will be issued with a reminder to use a Delay instead. Other options would include adding samples to a pulse instruction which are (0.+0.j) or setting the start time of the next pulse to schedule.duration + buffer.

• Passing in sympy.Basic, sympy.Expr and sympy.Matrix types as instruction parameters are deprecated and will be removed in a future release. You’ll need to convert the input to one of the supported types which are:

• int

• float

• complex

• str

• np.ndarray

##### Bug Fixes¶
• The Collect2qBlocks and CommutationAnalysis passes in the transpiler had been unable to process circuits containing Parameterized gates, preventing Parameterized circuits from being transpiled at optimization_level 2 or above. These passes have been corrected to treat Parameterized gates as opaque.

• The align_measures function had an issue where Measure stimulus pulses weren’t properly aligned with Acquire pulses, resulting in an error. This has been fixed.

• Uses of numpy.random.seed have been removed so that calls of qiskit functions do not affect results of future calls to numpy.random

• Fixed race condition occurring in the job monitor when job.queue_position() returns None. None is a valid return from job.queue_position().

• Backend support for memory=True now checked when that kwarg is passed. QiskitError results if not supported.

• When transpiling without a coupling map, there were no check in the amount of qubits of the circuit to transpile. Now the transpile process checks that the backend has enough qubits to allocate the circuit.

##### Other Notes¶
• The qiskit.result.marginal_counts() function replaces a similar utility function in qiskit-ignis qiskit.ignis.verification.tomography.marginal_counts(), which will be deprecated in a future qiskit-ignis release.

• All sympy parameter output type support have been been removed (or deprecated as noted) from qiskit-terra. This includes sympy type parameters in QuantumCircuit objects, qasm ast nodes, or Qobj objects.

No Change

No Change

No Change

#### IBM Q Provider 0.4¶

##### Prelude¶

The 0.4.0 release is the first release that makes use of all the features of the new IBM Q API. In particular, the IBMQJob class has been revamped in order to be able to retrieve more information from IBM Q, and a Job Manager class has been added for allowing a higher-level and more seamless usage of large or complex jobs. If you have not upgraded from the legacy IBM Q Experience or QConsole yet, please ensure to revisit the release notes for IBM Q Provider 0.3 (Qiskit 0.11) for more details on how to make the transition. The legacy accounts will no longer be supported as of this release.

##### New Features¶
###### Job modifications¶

The IBMQJob class has been revised, and now mimics more closely to the contents of a remote job along with new features:

• You can now assign a name to a job, by specifying IBMQBackend.run(..., job_name='...') when submitting a job. This name can be retrieved via IBMQJob.name() and can be used for filtering.

• Jobs can now be shared with other users at different levels (global, per hub, group or project) via an optional job_share_level parameter when submitting the job.

• IBMQJob instances now have more attributes, reflecting the contents of the remote IBM Q jobs. This implies that new attributes introduced by the IBM Q API will automatically and immediately be available for use (for example, job.new_api_attribute). The new attributes will be promoted to methods when they are considered stable (for example, job.name()).

• .error_message() returns more information on why a job failed.

• .queue_position() accepts a refresh parameter for forcing an update.

• .result() accepts an optional partial parameter, for returning partial results, if any, of jobs that failed. Be aware that Result methods, such as get_counts() will raise an exception if applied on experiments that failed.

Please note that the changes include some low-level modifications of the class. If you were creating the instances manually, note that:

• the signature of the constructor has changed to account for the new features.

• the .submit() method can no longer be called directly, and jobs are expected to be submitted either via the synchronous IBMQBackend.run() or via the Job Manager.

###### Job Manager¶

A new Job Manager (IBMQJobManager) has been introduced, as a higher-level mechanism for handling jobs composed of multiple circuits or pulse schedules. The Job Manager aims to provide a transparent interface, intelligently splitting the input into efficient units of work and taking full advantage of the different components. It will be expanded on upcoming versions, and become the recommended entry point for job submission.

Its .run() method receives a list of circuits or pulse schedules, and returns a ManagedJobSet instance, which can then be used to track the statuses and results of these jobs. For example:

from qiskit.providers.ibmq.managed import IBMQJobManager
from qiskit.circuit.random import random_circuit
from qiskit import IBMQ
from qiskit.compiler import transpile

backend = provider.backends.ibmq_ourense

circs = []
for _ in range(1000000):
circs.append(random_circuit(2, 2))
transpile(circs, backend=backend)

# Farm out the jobs.
jm = IBMQJobManager()
job_set = jm.run(circs, backend=backend, name='foo')

job_set.statuses()    # Gives a list of job statuses
job_set.report()    # Prints detailed job information
results = job_set.results()
counts = results.get_counts(5)   # Returns data for experiment 5

###### provider.backends modifications¶

The provider.backends member, which was previously a function that returned a list of backends, has been promoted to a service. This implies that it can be used both in the previous way, as a .backends() method, and also as a .backends attribute with expanded capabilities:

• it contains the existing backends from that provider as attributes, which can be used for autocompletion. For example:

my_backend = provider.get_backend('ibmq_qasm_simulator')


is equivalent to:

my_backend = provider.backends.ibmq_qasm_simulator

• the provider.backends.jobs() and provider.backends.retrieve_job() methods can be used for retrieving provider-wide jobs.

###### Other changes¶
• The backend.properties() function now accepts an optional datetime parameter. If specified, the function returns the backend properties closest to, but older than, the specified datetime filter.

• Some warnings have been toned down to logger.warning messages.

### Qiskit 0.13.0¶

#### Terra 0.10.0¶

##### Prelude¶

The 0.10.0 release includes several new features and bug fixes. The biggest change for this release is the addition of initial support for using Qiskit with trapped ion trap backends.

##### New Features¶
• Introduced new methods in QuantumCircuit which allows the seamless adding or removing of measurements at the end of a circuit.

measure_all()

Adds a barrier followed by a measure operation to all qubits in the circuit. Creates a ClassicalRegister of size equal to the number of qubits in the circuit, which store the measurements.

measure_active()

Adds a barrier followed by a measure operation to all active qubits in the circuit. A qubit is active if it has at least one other operation acting upon it. Creates a ClassicalRegister of size equal to the number of active qubits in the circuit, which store the measurements.

remove_final_measurements()

Removes all final measurements and preceeding barrier from a circuit. A measurement is considered “final” if it is not followed by any other operation, excluding barriers and other measurements. After the measurements are removed, if all of the classical bits in the ClassicalRegister are idle (have no operations attached to them), then the ClassicalRegister is removed.

Examples:

# Using measure_all()
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.measure_all()
circuit.draw()

# A ClassicalRegister with prefix measure was created.
# It has 2 clbits because there are 2 qubits to measure

┌───┐ ░ ┌─┐
q_0: |0>┤ H ├─░─┤M├───
└───┘ ░ └╥┘┌─┐
q_1: |0>──────░──╫─┤M├
░  ║ └╥┘
measure_0: 0 ═════════╩══╬═
║
measure_1: 0 ════════════╩═

# Using measure_active()
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.measure_active()
circuit.draw()

# This ClassicalRegister only has 1 clbit because only 1 qubit is active

┌───┐ ░ ┌─┐
q_0: |0>┤ H ├─░─┤M├
└───┘ ░ └╥┘
q_1: |0>──────░──╫─
░  ║
measure_0: 0 ═════════╩═

# Using remove_final_measurements()
# Assuming circuit_all and circuit_active are the circuits from the measure_all and
# measure_active examples above respectively

circuit_all.remove_final_measurements()
circuit_all.draw()
# The ClassicalRegister is removed because, after the measurements were removed,
# all of its clbits were idle

┌───┐
q_0: |0>┤ H ├
└───┘
q_1: |0>─────

circuit_active.remove_final_measurements()
circuit_active.draw()
# This will result in the same circuit

┌───┐
q_0: |0>┤ H ├
└───┘
q_1: |0>─────

• Initial support for executing experiments on ion trap backends has been added.

• An Rxx gate (rxx) and a global Mølmer–Sørensen gate (ms) have been added to the standard gate set.

• A Cnot to Rxx/Rx/Ry decomposer cnot_rxx_decompose and a single qubit Euler angle decomposer OneQubitEulerDecomposer have been added to the quantum_info.synthesis module.

• A transpiler pass MSBasisDecomposer has been added to unroll circuits defined over U3 and Cnot gates into a circuit defined over Rxx,Ry and Rx. This pass will be included in preset pass managers for backends which include the ‘rxx’ gate in their supported basis gates.

• The backends in qiskit.test.mock now contain a snapshot of real device calibration data. This is accessible via the properties() method for each backend. This can be used to test any code that depends on backend properties, such as noise-adaptive transpiler passes or device noise models for simulation. This will create a faster testing and development cycle without the need to go to live backends.

• Allows the Result class to return partial results. If a valid result schema is loaded that contains some experiments which succeeded and some which failed, this allows accessing the data from experiments that succeeded, while raising an exception for experiments that failed and displaying the appropriate error message for the failed results.

• An ax kwarg has been added to the following visualization functions:

• qiskit.visualization.plot_histogram

• qiskit.visualization.plot_state_paulivec

• qiskit.visualization.plot_state_qsphere

• qiskit.visualization.circuit_drawer (mpl backend only)

• qiskit.QuantumCircuit.draw (mpl backend only)

This kwarg is used to pass in a matplotlib.axes.Axes object to the visualization functions. This enables integrating these visualization functions into a larger visualization workflow. Also, if an ax kwarg is specified then there is no return from the visualization functions.

• An ax_real and ax_imag kwarg has been added to the following visualization functions:

• qiskit.visualization.plot_state_hinton

• qiskit.visualization.plot_state_city

These new kargs work the same as the newly added ax kwargs for other visualization functions. However because these plots use two axes (one for the real component, the other for the imaginary component). Having two kwargs also provides the flexibility to only generate a visualization for one of the components instead of always doing both. For example:

from matplotlib import pyplot as plt
from qiskit.visualization import plot_state_hinton

ax = plt.gca()

plot_state_hinton(psi, ax_real=ax)


will only generate a plot of the real component.

• A given pass manager now can be edited with the new method replace. This method allows to replace a particular stage in a pass manager, which can be handy when dealing with preset pass managers. For example, let’s edit the layout selector of the pass manager used at optimization level 0:

from qiskit.transpiler.preset_passmanagers.level0 import level_0_pass_manager
from qiskit.transpiler.transpile_config import TranspileConfig

pass_manager = level_0_pass_manager(TranspileConfig(coupling_map=CouplingMap([[0,1]])))

pass_manager.draw()

[0] FlowLinear: SetLayout
[1] Conditional: TrivialLayout
[2] FlowLinear: FullAncillaAllocation, EnlargeWithAncilla, ApplyLayout
[3] FlowLinear: Unroller


The layout selection is set in the stage [1]. Let’s replace it with DenseLayout:

from qiskit.transpiler.passes import DenseLayout

pass_manager.replace(1, DenseLayout(coupling_map), condition=lambda property_set: not property_set['layout'])
pass_manager.draw()

[0] FlowLinear: SetLayout
[1] Conditional: DenseLayout
[2] FlowLinear: FullAncillaAllocation, EnlargeWithAncilla, ApplyLayout
[3] FlowLinear: Unroller


If you want to replace it without any condition, you can use set-item shortcut:

pass_manager[1] = DenseLayout(coupling_map)
pass_manager.draw()

[0] FlowLinear: SetLayout
[1] FlowLinear: DenseLayout
[2] FlowLinear: FullAncillaAllocation, EnlargeWithAncilla, ApplyLayout
[3] FlowLinear: Unroller

• Introduced a new pulse command Delay which may be inserted into a pulse Schedule. This command accepts a duration and may be added to any Channel. Other commands may not be scheduled on a channel during a delay.

The delay can be added just like any other pulse command. For example:

from qiskit import pulse

dc0 = pulse.DriveChannel(0)

delay = pulse.Delay(1)
test_pulse = pulse.SamplePulse([1.0])

sched = pulse.Schedule()
sched += test_pulse(dc0).shift(1)

# build padded schedule by hand
ref_sched = delay(dc0) | sched



One may also pass additional channels to be padded and a time to pad until, for example:

from qiskit import pulse

dc0 = pulse.DriveChannel(0)
dc1 = pulse.DriveChannel(1)

delay = pulse.Delay(1)
test_pulse = pulse.SamplePulse([1.0])

sched = pulse.Schedule()
sched += test_pulse(dc0).shift(1)

# build padded schedule by hand
ref_sched = delay(dc0) | delay(dc1) |  sched

# pad schedule across both channels until up until the first time step


• Assignments and modifications to the data attribute of qiskit.QuantumCircuit objects are now validated following the same rules used throughout the QuantumCircuit API. This was done to improve the performance of the circuits API since we can now assume the data attribute is in a known format. If you were manually modifying the data attribute of a circuit object before this may no longer work if your modifications resulted in an data structure other than the list of instructions with context in the format [(instruction, qargs, cargs)]

• The transpiler default passmanager for optimization level 2 now uses the DenseLayout layout selection mechanism by default instead of NoiseAdaptiveLayout. The Denselayout pass has also been modified to be made noise-aware.

• The deprecated DeviceSpecification class has been removed. Instead you should use the PulseChannelSpec. For example, you can run something like:

device = pulse.PulseChannelSpec.from_backend(backend)
device.drives[0]    # for DeviceSpecification, this was device.q[0].drive
device.memoryslots  # this was device.mem

• The deprecated module qiskit.pulse.ops has been removed. Use Schedule and Instruction methods directly. For example, rather than:

ops.union(schedule_0, schedule_1)
ops.union(instruction, schedule)  # etc


schedule_0.union(schedule_1)
instruction.union(schedule)


This same pattern applies to other ops functions: insert, shift, append, and flatten.

##### Deprecation Notes¶
• Using the control property of qiskit.circuit.Instruction for classical control is now deprecated. In the future this property will be used for quantum control. Classically conditioned operations will instead be handled by the condition property of qiskit.circuit.Instruction.

• Support for setting qiskit.circuit.Instruction parameters with an object of type qiskit.qasm.node.Node has been deprecated. Node objects that were previously used as parameters should be converted to a supported type prior to initializing a new Instruction object or calling the Instruction.params setter. Supported types are int, float, complex, str, qiskit.circuit.ParameterExpression, or numpy.ndarray.

• In the qiskit 0.9.0 release the representation of bits (both qubits and classical bits) changed from tuples of the form (register, index) to be instances of the classes qiskit.circuit.Qubit and qiskit.circuit.Clbit. For backwards compatibility comparing the equality between a legacy tuple and the bit classes was supported as everything transitioned from tuples to being objects. This support is now deprecated and will be removed in the future. Everything should use the bit classes instead of tuples moving forward.

• When the mpl output is used for either qiskit.QuantumCircuit.draw() or qiskit.visualization.circuit_drawer() and the style kwarg is used, passing in unsupported dictionary keys as part of the style dictionary is now deprecated. Where these unknown arguments were previously silently ignored, in the future, unsupported keys will raise an exception.

• The line length kwarg for the qiskit.QuantumCircuit.draw() method and the qiskit.visualization.circuit_drawer() function with the text output mode is deprecated. It has been replaced by the fold kwarg which will behave identically for the text output mode (but also now supports the mpl output mode too). line_length will be removed in a future release so calls should be updated to use fold instead.

• The fold field in the style dict kwarg for the qiskit.QuantumCircuit.draw() method and the qiskit.visualization.circuit_drawer() function has been deprecated. It has been replaced by the fold kwarg on both functions. This kwarg behaves identically to the field in the style dict.

##### Bug Fixes¶
• Instructions layering which underlies all types of circuit drawing has changed to address right/left justification. This sometimes results in output which is topologically equivalent to the rendering in prior versions but visually different than previously rendered. Fixes issue #2802

• Add memory_slots to QobjExperimentHeader of pulse Qobj. This fixes a bug in the data format of meas_level=2 results of pulse experiments. Measured quantum states are returned as a bit string with zero padding based on the number set for memory_slots.

• Fixed the visualization of the rzz gate in the latex circuit drawer to match the cu1 gate to reflect the symmetry in the rzz gate. The fix is based on the cds command of the qcircuit latex package. Fixes issue #1957

##### Other Notes¶
• matplotlib.figure.Figure objects returned by visualization functions are no longer always closed by default. Instead the returned figure objects are only closed if the configured matplotlib backend is an inline jupyter backend(either set with %matplotlib inline or %matplotlib notebook). Output figure objects are still closed with these backends to avoid duplicate outputs in jupyter notebooks (which is why the Figure.close() were originally added).

No Change

No Change

No Change

No Change

### Qiskit 0.12.0¶

#### Terra 0.9¶

##### Prelude¶

The 0.9 release includes many new features and many bug fixes. The biggest changes for this release are new debugging capabilities for PassManagers. This includes a function to visualize a PassManager, the ability to add a callback function to a PassManager, and logging of passes run in the PassManager. Additionally, this release standardizes the way that you can set an initial layout for your circuit. So now you can leverage initial_layout the kwarg parameter on qiskit.compiler.transpile() and qiskit.execute() and the qubits in the circuit will get laid out on the desire qubits on the device. Visualization of circuits will now also show this clearly when visualizing a circuit that has been transpiled with a layout.

##### New Features¶
• A DAGCircuit object (i.e. the graph representation of a QuantumCircuit where operation dependencies are explicit) can now be visualized with the .draw() method. This is in line with Qiskit’s philosophy of easy visualization. Other objects which support a .draw() method are QuantumCircuit, PassManager, and Schedule.

• Added a new visualization function qiskit.visualization.plot_error_map() to plot the error map for a given backend. It takes in a backend object from the qiskit-ibmq-provider and will plot the current error map for that device.

• Both qiskit.QuantumCircuit.draw() and qiskit.visualization.circuit_drawer() now support annotating the qubits in the visualization with layout information. If the QuantumCircuit object being drawn includes layout metadata (which is normally only set on the circuit output from transpile() calls) then by default that layout will be shown on the diagram. This is done for all circuit drawer backends. For example:

from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
from qiskit.compiler import transpile

qr = QuantumRegister(2, 'userqr')
cr = ClassicalRegister(2, 'c0')
qc = QuantumCircuit(qr, cr)
qc.h(qr[0])
qc.cx(qr[0], qr[1])
qc.y(qr[0])
qc.x(qr[1])
qc.measure(qr, cr)

# Melbourne coupling map
coupling_map = [[1, 0], [1, 2], [2, 3], [4, 3], [4, 10], [5, 4],
[5, 6], [5, 9], [6, 8], [7, 8], [9, 8], [9, 10],
[11, 3], [11, 10], [11, 12], [12, 2], [13, 1],
[13, 12]]
qc_result = transpile(qc, basis_gates=['u1', 'u2', 'u3', 'cx', 'id'],
coupling_map=coupling_map, optimization_level=0)
qc.draw(output='text')


will yield a diagram like:

                  ┌──────────┐┌──────────┐┌───┐┌──────────┐┌──────────────────┐┌─┐
(userqr0) q0|0>┤ U2(0,pi) ├┤ U2(0,pi) ├┤ X ├┤ U2(0,pi) ├┤ U3(pi,pi/2,pi/2) ├┤M├───
├──────────┤└──────────┘└─┬─┘├──────────┤└─┬─────────────┬──┘└╥┘┌─┐
(userqr1) q1|0>┤ U2(0,pi) ├──────────────■──┤ U2(0,pi) ├──┤ U3(pi,0,pi) ├────╫─┤M├
└──────────┘                 └──────────┘  └─────────────┘    ║ └╥┘
(ancilla0) q2|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla1) q3|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla2) q4|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla3) q5|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla4) q6|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla5) q7|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla6) q8|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla7) q9|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla8) q10|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla9) q11|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla10) q12|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
(ancilla11) q13|0>──────────────────────────────────────────────────────────────╫──╫─
║  ║
c0_0: 0 ══════════════════════════════════════════════════════════════╩══╬═
║
c0_1: 0 ═════════════════════════════════════════════════════════════════╩═


If you do not want the layout to be shown on transpiled circuits (or any other circuits with a layout set) there is a new boolean kwarg for both functions, with_layout (which defaults True), which when set False will disable the layout annotation in the output circuits.

• A new analysis pass CountOpsLongest was added to retrieve the number of operations on the longest path of the DAGCircuit. When used it will add a count_ops_longest_path key to the property set dictionary. You can add it to your a passmanager with something like:

from qiskit.transpiler.passes import CountOpsLongestPath
from qiskit.transpiler.passes import CxCancellation
from qiskit.transpiler import PassManager

pm = PassManager()
pm.append(CountOpsLongestPath())


and then access the longest path via the property set value with something like:

pm.append(
CxCancellation(),
condition=lambda property_set: property_set[
'count_ops_longest_path'] < 5)


which will set a condition on that pass based on the longest path.

• Two new functions, sech() and sech_deriv() were added to the pulse library module qiskit.pulse.pulse_lib for creating an unnormalized hyperbolic secant SamplePulse object and an unnormalized hyperbolic secant derviative SamplePulse object resepctively.

• A new kwarg option vertical_compression was added to the QuantumCircuit.draw() method and the qiskit.visualization.circuit_drawer() function. This option only works with the text backend. This option can be set to either high, medium (the default), or low to adjust how much vertical space is used by the output visualization.

• A new kwarg boolean option idle_wires was added to the QuantumCircuit.draw() method and the qiskit.visualization.circuit_drawer() function. It works for all drawer backends. When idle_wires is set False in a drawer call the drawer will not draw any bits that do not have any circuit elements in the output quantum circuit visualization.

• A new PassManager visualizer function qiskit.visualization.pass_mamanger_drawer() was added. This function takes in a PassManager object and will generate a flow control diagram of all the passes run in the PassManager.

• When creating a PassManager you can now specify a callback function that if specified will be run after each pass is executed. This function gets passed a set of kwargs on each call with the state of the pass manager after each pass execution. Currently these kwargs are:

• pass_ (Pass): the pass being run

• dag (DAGCircuit): the dag output of the pass

• time (float): the time to execute the pass

• property_set (PropertySet): the property set

• count (int): the index for the pass execution

However, it’s worth noting that while these arguments are set for the 0.9 release they expose the internals of the pass manager and are subject to change in future release.

For example you can use this to create a callback function that will visualize the circuit output after each pass is executed:

from qiskit.transpiler import PassManager

def my_callback(**kwargs):
print(kwargs['dag'])

pm = PassManager(callback=my_callback)


Additionally you can specify the callback function when using qiskit.compiler.transpile():

from qiskit.compiler import transpile

def my_callback(**kwargs):
print(kwargs['pass'])

transpile(circ, callback=my_callback)

• A new method filter() was added to the qiskit.pulse.Schedule class. This enables filtering the instructions in a schedule. For example, filtering by instruction type:

from qiskit.pulse import Schedule
from qiskit.pulse.commands import Acquire
from qiskit.pulse.commands import AcquireInstruction
from qiskit.pulse.commands import FrameChange

sched = Schedule(name='MyExperiment')
sched.insert(0, FrameChange(phase=-1.57)(device))
sched.insert(60, Acquire(5))
acquire_sched = sched.filter(instruction_types=[AcquireInstruction])

• Additional decomposition methods for several types of gates. These methods will use different decomposition techniques to break down a gate into a sequence of CNOTs and single qubit gates. The following methods are added:

Method

Description

QuantumCircuit.iso()

Add an arbitrary isometry from m to n qubits to a circuit. This allows for attaching arbitrary unitaries on n qubits (m=n) or to prepare any state of n qubits (m=0)

QuantumCircuit.diag_gate()

Add a diagonal gate to the circuit

QuantumCircuit.squ()

Decompose an arbitrary 2x2 unitary into three rotation gates and add to a circuit

QuantumCircuit.ucg()

Attach an uniformly controlled gate (also called a multiplexed gate) to a circuit

QuantumCircuit.ucx()

Attach a uniformly controlled (also called multiplexed) Rx rotation gate to a circuit

QuantumCircuit.ucy()

Attach a uniformly controlled (also called multiplexed) Ry rotation gate to a circuit

QuantumCircuit.ucz()

Attach a uniformly controlled (also called multiplexed) Rz rotation gate to a circuit

• Addition of Gray-Synth and Patel–Markov–Hayes algorithms for synthesis of CNOT-Phase and CNOT-only linear circuits. These functions allow the synthesis of circuits that consist of only CNOT gates given a linear function or a circuit that consists of only CNOT and phase gates given a matrix description.

• A new function random_circuit was added to the qiskit.circuit.random module. This function will generate a random circuit of a specified size by randomly selecting different gates and adding them to the circuit. For example, you can use this to generate a 5 qubit circuit with a depth of 10 using:

from qiskit.circuit.random import random_circuit

circ = random_circuit(5, 10)

• A new kwarg output_names was added to the qiskit.compiler.transpile() function. This kwarg takes in a string or a list of strings and uses those as the value of the circuit name for the output circuits that get returned by the transpile() call. For example:

from qiskit.compiler import transpile
my_circs = [circ_a, circ_b]
tcirc_a, tcirc_b = transpile(my_circs,
output_names=['Circuit A', 'Circuit B'])


the name attribute on tcirc_a and tcirc_b will be 'Circuit A' and 'Circuit B' respectively.

• A new method equiv() was added to the qiskit.quantum_info.Operator and qiskit.quantum_info.Statevector classes. These methods are used to check whether a second Operator object or Statevector is equivalent up to global phase.

• The user config file has several new options:

• The circuit_drawer field now accepts an auto value. When set as the value for the circuit_drawer field the default drawer backend will be mpl if it is available, otherwise the text backend will be used.

• A new field circuit_mpl_style can be used to set the default style used by the matplotlib circuit drawer. Valid values for this field are bw and default to set the default to a black and white or the default color style respectively.

• A new field transpile_optimization_level can be used to set the default transpiler optimization level to use for calls to qiskit.compiler.transpile(). The value can be set to either 0, 1, 2, or 3.

• Introduced a new pulse command Delay which may be inserted into a pulse Schedule. This command accepts a duration and may be added to any Channel. Other commands may not be scheduled on a channel during a delay.

The delay can be added just like any other pulse command. For example:

from qiskit import pulse

drive_channel = pulse.DriveChannel(0)
delay = pulse.Delay(20)

sched = pulse.Schedule()
sched += delay(drive_channel)

• The previously deprecated qiskit._util module has been removed. qiskit.util should be used instead.

• The QuantumCircuit.count_ops() method now returns an OrderedDict object instead of a dict. This should be compatible for most use cases since OrderedDict is a dict subclass. However type checks and other class checks might need to be updated.

• The DAGCircuit.width() method now returns the total number quantum bits and classical bits. Before it would only return the number of quantum bits. If you require just the number of quantum bits you can use DAGCircuit.num_qubits() instead.

• The function DAGCircuit.num_cbits() has been removed. Instead you can use DAGCircuit.num_clbits().

• Individual quantum bits and classical bits are no longer represented as (register, index) tuples. They are now instances of Qubit and Clbit classes. If you’re dealing with individual bits make sure that you update any usage or type checks to look for these new classes instead of tuples.

• The preset passmanager classes qiskit.transpiler.preset_passmanagers.default_pass_manager and qiskit.transpiler.preset_passmanagers.default_pass_manager_simulator (which were the previous default pass managers for qiskit.compiler.transpile() calls) have been removed. If you were manually using this pass managers switch to the new default, qiskit.transpile.preset_passmanagers.level1_pass_manager.

• The LegacySwap pass has been removed. If you were using it in a custom pass manager, it’s usage can be replaced by the StochasticSwap pass, which is a faster more stable version. All the preset passmanagers have been updated to use StochasticSwap pass instead of the LegacySwap.

• The following deprecated qiskit.dagcircuit.DAGCircuit methods have been removed:

• DAGCircuit.get_qubits() - Use DAGCircuit.qubits() instead

• DAGCircuit.get_bits() - Use DAGCircuit.clbits() instead

• DAGCircuit.qasm() - Use a combination of qiskit.converters.dag_to_circuit() and QuantumCircuit.qasm(). For example:

from qiskit.dagcircuit import DAGCircuit
from qiskit.converters import dag_to_circuit
my_dag = DAGCircuit()
qasm = dag_to_circuit(my_dag).qasm()

• DAGCircuit.get_op_nodes() - Use DAGCircuit.op_nodes() instead. Note that the return type is a list of DAGNode objects for op_nodes() instead of the list of tuples previously returned by get_op_nodes().

• DAGCircuit.get_gate_nodes() - Use DAGCircuit.gate_nodes() instead. Note that the return type is a list of DAGNode objects for gate_nodes() instead of the list of tuples previously returned by get_gate_nodes().

• DAGCircuit.get_named_nodes() - Use DAGCircuit.named_nodes() instead. Note that the return type is a list of DAGNode objects for named_nodes() instead of the list of node_ids previously returned by get_named_nodes().

• DAGCircuit.get_2q_nodes() - Use DAGCircuit.twoQ_gates() instead. Note that the return type is a list of DAGNode objects for twoQ_gates() instead of the list of data_dicts previously returned by get_2q_nodes().

• DAGCircuit.get_3q_or_more_nodes() - Use DAGCircuit.threeQ_or_more_gates() instead. Note that the return type is a list of DAGNode objects for threeQ_or_more_gates() instead of the list of tuples previously returned by get_3q_or_more_nodes().

• The following qiskit.dagcircuit.DAGCircuit methods had deprecated support for accepting a node_id as a parameter. This has been removed and now only DAGNode objects are accepted as input:

• successors()

• predecessors()

• ancestors()

• descendants()

• bfs_successors()

• quantum_successors()

• remove_op_node()

• remove_ancestors_of()

• remove_descendants_of()

• remove_nonancestors_of()

• remove_nondescendants_of()

• substitute_node_with_dag()

• The qiskit.dagcircuit.DAGCircuit method rename_register() has been removed. This was unused by all the qiskit code. If you were relying on it externally you’ll have to re-implement is an external function.

• The qiskit.dagcircuit.DAGCircuit property multi_graph has been removed. Direct access to the underlying networkx multi_graph object isn’t supported anymore. The API provided by the DAGCircuit class should be used instead.

• The deprecated exception class qiskit.qiskiterror.QiskitError has been removed. Instead you should use qiskit.exceptions.QiskitError.

• The boolean kwargs, ignore_requires and ignore_preserves from the qiskit.transpiler.PassManager constructor have been removed. These are no longer valid options.

• The module qiskit.tools.logging has been removed. This module was not used by anything and added nothing over the interfaces that Python’s standard library logging module provides. If you want to set a custom formatter for logging use the standard library logging module instead.

• The CompositeGate class has been removed. Instead you should directly create a instruction object from a circuit and append that to your circuit. For example, you can run something like:

custom_gate_circ = qiskit.QuantumCircuit(2)
custom_gate_circ.x(1)
custom_gate_circ.h(0)
custom_gate_circ.cx(0, 1)
custom_gate = custom_gate_circ.to_instruction()

• The previously deprecated kwargs, seed and config for qiskit.compiler.assemble() have been removed use seed_simulator and run_config respectively instead.

• The previously deprecated converters qiskit.converters.qobj_to_circuits() and qiskit.converters.circuits_to_qobj() have been removed. Use qiskit.assembler.disassemble() and qiskit.compiler.assemble() respectively instead.

• The previously deprecated kwarg seed_mapper for qiskit.compiler.transpile() has been removed. Instead you should use seed_transpiler

• The previously deprecated kwargs seed, seed_mapper, config, and circuits for the qiskit.execute() function have been removed. Use seed_simulator, seed_transpiler, run_config, and experiments arguments respectively instead.

• The previously deprecated qiskit.tools.qcvv module has been removed use qiskit-ignis instead.

• The previously deprecated functions qiskit.transpiler.transpile() and qiskit.transpiler.transpile_dag() have been removed. Instead you should use qiskit.compiler.transpile. If you were using transpile_dag() this can be replaced by running:

circ = qiskit.converters.dag_to_circuit(dag)
out_circ = qiskit.compiler.transpile(circ)
qiskit.converters.circuit_to_dag(out_circ)

• The previously deprecated function qiskit.compile() has been removed instead you should use qiskit.compiler.transpile() and qiskit.compiler.assemble().

• The jupyter cell magic %%qiskit_progress_bar from qiskit.tools.jupyter has been changed to a line magic. This was done to better reflect how the magic is used and how it works. If you were using the %%qiskit_progress_bar cell magic in an existing notebook, you will have to update this to be a line magic by changing it to be %qiskit_progress_bar instead. Everything else should behave identically.

• The deprecated function qiskit.tools.qi.qi.random_unitary_matrix() has been removed. You should use the qiskit.quantum_info.random.random_unitary() function instead.

• The deprecated function qiskit.tools.qi.qi.random_density_matrix() has been removed. You should use the qiskit.quantum_info.random.random_density_matrix() function instead.

• The deprecated function qiskit.tools.qi.qi.purity() has been removed. You should the qiskit.quantum_info.purity() function instead.

• The deprecated QuantumCircuit._attach() method has been removed. You should use QuantumCircuit.append() instead.

• The qiskit.qasm.Qasm method get_filename() has been removed. You can use the return_filename() method instead.

• The deprecated qiskit.mapper module has been removed. The list of functions and classes with their alternatives are:

• qiskit.mapper.CouplingMap: qiskit.transpiler.CouplingMap should be used instead.

• qiskit.mapper.Layout: qiskit.transpiler.Layout should be used instead

• qiskit.mapper.compiling.euler_angles_1q(): qiskit.quantum_info.synthesis.euler_angles_1q() should be used instead

• qiskit.mapper.compiling.two_qubit_kak(): qiskit.quantum_info.synthesis.two_qubit_cnot_decompose() should be used instead.

The deprecated exception classes qiskit.mapper.exceptions.CouplingError and qiskit.mapper.exceptions.LayoutError don’t have an alternative since they serve no purpose without a qiskit.mapper module.

• The qiskit.pulse.samplers module has been moved to qiskit.pulse.pulse_lib.samplers. You will need to update imports of qiskit.pulse.samplers to qiskit.pulse.pulse_lib.samplers.

• seaborn is now a dependency for the function qiskit.visualization.plot_state_qsphere(). It is needed to generate proper angular color maps for the visualization. The qiskit-terra[visualization] extras install target has been updated to install seaborn>=0.9.0 If you are using visualizations and specifically the plot_state_qsphere() function you can use that to install seaborn or just manually run pip install seaborn>=0.9.0

• The previously deprecated functions qiksit.visualization.plot_state and qiskit.visualization.iplot_state have been removed. Instead you should use the specific function for each plot type. You can refer to the following tables to map the deprecated functions to their equivalent new ones:

Qiskit Terra 0.6

Qiskit Terra 0.7+

plot_state(rho)

plot_state_city(rho)

plot_state(rho, method=’city’)

plot_state_city(rho)

plot_state(rho, method=’paulivec’)

plot_state_paulivec(rho)

plot_state(rho, method=’qsphere’)

plot_state_qsphere(rho)

plot_state(rho, method=’bloch’)

plot_bloch_multivector(rho)

plot_state(rho, method=’hinton’)

plot_state_hinton(rho)

• The pylatexenc and pillow dependencies for the latex and latex_source circuit drawer backends are no longer listed as requirements. If you are going to use the latex circuit drawers ensure you have both packages installed or use the setuptools extras to install it along with qiskit-terra:

pip install qiskit-terra[visualization]

• The root of the qiskit namespace will now emit a warning on import if either qiskit.IBMQ or qiskit.Aer could not be setup. This will occur whenever anything in the qiskit namespace is imported. These warnings were added to make it clear for users up front if they’re running qiskit and the qiskit-aer and qiskit-ibmq-provider packages could not be found. It’s not always clear if the packages are missing or python packaging/pip installed an element incorrectly until you go to use them and get an empty ImportError. These warnings should make it clear up front if there these commonly used aliases are missing.

However, for users that choose not to use either qiskit-aer or qiskit-ibmq-provider this might cause additional noise. For these users these warnings are easily suppressable using Python’s standard library warnings. Users can suppress the warnings by putting these two lines before any imports from qiskit:

import warnings
warnings.filterwarnings('ignore', category=RuntimeWarning,
module='qiskit')


This will suppress the warnings emitted by not having qiskit-aer or qiskit-ibmq-provider installed, but still preserve any other warnings emitted by qiskit or any other package.

##### Deprecation Notes¶
• The U and CX gates have been deprecated. If you’re using these gates in your code you should update them to use u3 and cx instead. For example, if you’re using the circuit gate functions circuit.u_base() and circuit.cx_base() you should update these to be circuit.u3() and circuit.cx() respectively.

• The u0 gate has been deprecated in favor of using multiple iden gates and it will be removed in the future. If you’re using the u0 gate in your circuit you should update your calls to use iden. For example, f you were using circuit.u0(2) in your circuit before that should be updated to be:

circuit.iden()
circuit.iden()


• The qiskit.pulse.DeviceSpecification class is deprecated now. Instead you should use qiskit.pulse.PulseChannelSpec.

• Accessing a qiskit.circuit.Qubit, qiskit.circuit.Clbit, or qiskit.circuit.Bit class by index is deprecated (for compatibility with the (register, index) tuples that these classes replaced). Instead you should use the register and index attributes.

• Passing in a bit to the qiskit.QuantumCircuit method append as a tuple (register, index) is deprecated. Instead bit objects should be used directly.

• Accessing the elements of a qiskit.transpiler.Layout object with a tuple (register, index) is deprecated. Instead a bit object should be used directly.

• The qiskit.transpiler.Layout constructor method qiskit.transpiler.Layout.from_tuplelist() is deprecated. Instead the constructor qiskit.transpiler.Layout.from_qubit_list() should be used.

• The module qiskit.pulse.ops has been deprecated. All the functions it provided:

• union

• flatten

• shift

• insert

• append

have equivalent methods available directly on the qiskit.pulse.Schedule and qiskit.pulse.Instruction classes. Those methods should be used instead.

• The qiskit.qasm.Qasm method get_tokens() is deprecated. Instead you should use the generate_tokens() method.

• The qiskit.qasm.qasmparser.QasmParser method get_tokens() is deprecated. Instead you should use the read_tokens() method.

• The as_dict() method for the Qobj class has been deprecated and will be removed in the future. You should replace calls to it with to_dict() instead.

##### Bug Fixes¶
• The definition of the CU3Gate has been changed to be equivalent to the canonical definition of a controlled U3Gate.

• The handling of layout in the pass manager has been standardized. This fixes several reported issues with handling layout. The initial_layout kwarg parameter on qiskit.compiler.transpile() and qiskit.execute() will now lay out your qubits from the circuit onto the desired qubits on the device when transpiling circuits.

• Support for n-qubit unitaries was added to the BasicAer simulator and unitary (arbitrary unitary gates) was added to the set of basis gates for the simulators

• The qiskit.visualization.plost_state_qsphere() has been updated to fix several issues with it. Now output Q Sphere visualization will be correctly generated and the following aspects have been updated:

• All complementary basis states are antipodal

• Phase is indicated by color of line and marker on sphere’s surface

• Probability is indicated by translucency of line and volume of marker on

sphere’s surface

##### Other Notes¶
• The default PassManager for qiskit.compiler.transpile() and qiskit.execute() has been changed to optimization level 1 pass manager defined at qiskit.transpile.preset_passmanagers.level1_pass_manager.

• All the circuit drawer backends now will express gate parameters in a circuit as common fractions of pi in the output visualization. If the value of a parameter can be expressed as a fraction of pi that will be used instead of the numeric equivalent.

• When using qiskit.assembler.assemble_schedules() if you do not provide the number of memory_slots to use the number will be inferred based on the number of acquisitions in the input schedules.

• The deprecation warning on the qiskit.dagcircuit.DAGCircuit property node_counter has been removed. The behavior change being warned about was put into effect when the warning was added, so warning that it had changed served no purpose.

• Calls to PassManager.run() now will emit python logging messages at the INFO level for each pass execution. These messages will include the Pass name and the total execution time of the pass. Python’s standard logging was used because it allows Qiskit-Terra’s logging to integrate in a standard way with other applications and libraries. All logging for the transpiler occurs under the qiskit.transpiler namespace, as used by logging.getLogger('qiskit.transpiler). For example, to turn on DEBUG level logging for the transpiler you can run:

import logging

logging.basicConfig()
logging.getLogger('qiskit.transpiler').setLevel(logging.DEBUG)


which will set the log level for the transpiler to DEBUG and configure those messages to be printed to stderr.

#### Aer 0.3¶

• There’s a new high-performance Density Matrix Simulator that can be used in conjunction with our noise models, to better simulate real world scenarios.

• We have added a Matrix Product State (MPS) simulator. MPS allows for efficient simulation of several classes of quantum circuits, even under presence of strong correlations and highly entangled states. For cases amenable to MPS, circuits with several hundred qubits and more can be exactly simulated, e.g., for the purpose of obtaining expectation values of observables.

• Snapshots can be performed in all of our simulators.

• Now we can measure sampling circuits with read-out errors too, not only ideal circuits.

• We have increased some circuit optimizations with noise presence.

• A better 2-qubit error aproximations have been included.

• Included some tools for making certain noisy simulations easier to craft and faster to simulate.

• Increased performance with simulations that require less floating point numerical precision.

#### Ignis 0.2¶

##### Bug Fixes¶
• Fixed a bug in RB fit error

• Fixed a bug in the characterization fitter when selecting a qubit index to fit

##### Other Notes¶
• Measurement mitigation now operates in parallel when applied to multiple results

• Guess values for RB fitters are improved

#### Aqua 0.6¶

• Relative-Phase Toffoli gates rccx (with 2 controls) and rcccx (with 3 controls).

• Variational form RYCRX

• A new 'basic-no-ancilla' mode to mct.

• Multi-controlled rotation gates mcrx, mcry, and mcrz as a general u3 gate is not supported by graycode implementation

• Chemistry: ROHF open-shell support

• Supported for all drivers: Gaussian16, PyQuante, PySCF and PSI4

• HartreeFock initial state, UCCSD variational form and two qubit reduction for parity mapping now support different alpha and beta particle numbers for open shell support

• Chemistry: UHF open-shell support

• Supported for all drivers: Gaussian16, PyQuante, PySCF and PSI4

• QMolecule extended to include integrals, coefficients etc for separate beta

• Chemistry: QMolecule extended with integrals in atomic orbital basis to facilitate common access to these for experimentation

• Supported for all drivers: Gaussian16, PyQuante, PySCF and PSI4

• Chemistry: Additional PyQuante and PySCF driver configuration

• Convergence tolerance and max convergence iteration controls.

• For PySCF initial guess choice

• Chemistry: Processing output added to debug log from PyQuante and PySCF computations (Gaussian16 and PSI4 outputs were already added to debug log)

• Chemistry: Merged qiskit-chemistry into qiskit-aqua

• Add MatrixOperator, WeightedPauliOperator and TPBGroupedPauliOperator class.

• Add evolution_instruction function to get registerless instruction of time evolution.

• Add op_converter module to unified the place in charge of converting different types of operators.

• Add Z2Symmetries class to encapsulate the Z2 symmetries info and has helper methods for tapering an Operator.

• Amplitude Estimation: added maximum likelihood postprocessing and confidence interval computation.

• Maximum Likelihood Amplitude Estimation (MLAE): Implemented new algorithm for amplitude estimation based on maximum likelihood estimation, which reduces number of required qubits and circuit depth.

• Added (piecewise) linearly and polynomially controlled Pauli-rotation circuits.

• Add q_equation_of_motion to study excited state of a molecule, and add two algorithms to prepare the reference state.

##### Changed¶
• Improve mct’s 'basic' mode by using relative-phase Toffoli gates to build intermediate results.

• Adapt to Qiskit Terra’s newly introduced Qubit class.

• Prevent QPE/IQPE from modifying input Operator objects.

• The PyEDA dependency was removed; corresponding oracles’ underlying logic operations are now handled by SymPy.

• Refactor the Operator class, each representation has its own class MatrixOperator, WeightedPauliOperator and TPBGroupedPauliOperator.

• The power in evolution_instruction was applied on the theta on the CRZ gate directly, the new version repeats the circuits to implement power.

• CircuitCache is OFF by default, and it can be set via environment variable now QISKIT_AQUA_CIRCUIT_CACHE.

##### Bug Fixes¶
• A bug where TruthTableOracle would build incorrect circuits for truth tables with only a single 1 value.

• A bug caused by PyEDA’s indeterminism.

• A bug with QPE/IQPE’s translation and stretch computation.

• Chemistry: Bravyi-Kitaev mapping fixed when num qubits was not a power of 2

• Setup initial_layout in QuantumInstance via a list.

##### Removed¶
• General multi-controlled rotation gate mcu3 is removed and replaced by multi-controlled rotation gates mcrx, mcry, and mcrz

##### Deprecated¶
• The Operator class is deprecated, in favor of using MatrixOperator, WeightedPauliOperator and TPBGroupedPauliOperator.

No change

### Qiskit 0.11.1¶

We have bumped up Qiskit micro version to 0.11.1 because IBM Q Provider has bumped its micro version as well.

No Change

No change

No Change

#### Aqua 0.5¶

qiskit-aqua has been updated to 0.5.3 to fix code related to changes in how gates inverses are done.

#### IBM Q Provider 0.3¶

The IBMQProvider has been updated to version 0.3.1 to fix backward compatibility issues and work with the default 10 job limit in single calls to the IBM Q API v2.

### Qiskit 0.11¶

We have bumped up Qiskit minor version to 0.11 because IBM Q Provider has bumped up its minor version too. On Aer, we have jumped from 0.2.1 to 0.2.3 because there was an issue detected right after releasing 0.2.2 and before Qiskit 0.11 went online.

No Change

#### Aer 0.2¶

##### New features¶
• Added support for multi-controlled phase gates

• Added optimized anti-diagonal single-qubit gates

##### Improvements¶
• Introduced a technique called Fusion that increments performance of circuit execution Tuned threading strategy to gain performance in most common scenarios.

• Some of the already implemented error models have been polished.

No Change

No Change

#### IBM Q Provider 0.3¶

The IBMQProvider has been updated in order to default to using the new IBM Q Experience v2. Accessing the legacy IBM Q Experience v1 and QConsole will still be supported during the 0.3.x line until its final deprecation one month from the release. It is encouraged to update to the new IBM Q Experience to take advantage of the new functionality and features.

##### Updating to the new IBM Q Experience v2¶

If you have credentials for the legacy IBM Q Experience stored on disk, you can make use of the interactive helper:

from qiskit import IBMQ

IBMQ.update_account()


For more complex cases or fine tuning your configuration, the following methods are available:

• the IBMQ.delete_accounts() can be used for resetting your configuration file.

• the IBMQ.save_account('MY_TOKEN') method can be used for saving your credentials, following the instructions in the IBM Q Experience v2 account page.

When using the new IBM Q Experience v2 through the provider, access to backends is done via individual provider instances (as opposed to accessing them directly through the qiskit.IBMQ object as in previous versions), which allows for more granular control over the project you are using.

You can get a reference to the providers that you have access to using the IBMQ.providers() and IBMQ.get_provider() methods:

from qiskit import IBMQ

my_providers = IBMQ.providers()
provider_2 = IBMQ.get_provider(hub='A', group='B', project='C')


For convenience, IBMQ.load_account() and IBMQ.enable_account() will return a provider for the open access project, which is the default in the new IBM Q Experience v2.

For example, the following program in previous versions:

from qiskit import IBMQ

backend = IBMQ.get_backend('ibmqx4')
backend_2 = IBMQ.get_backend('ibmq_qasm_simulator', hub='HUB2')


Would be equivalent to the following program in the current version:

from qiskit import IBMQ

backend = provider.get_backend('ibmqx4')
provider_2 = IBMQ.get_provider(hub='HUB2')
backend_2 = provider_2.get_backend('ibmq_qasm_simulator')


You can find more information and details in the IBM Q Provider documentation.

### Qiskit 0.10¶

No Change

No Change

No Change

No Change

#### IBM Q Provider 0.2¶

##### New Features¶
• The IBMQProvider supports connecting to the new version of the IBM Q API. Please note support for this version is still experimental #78.

• Added support for Circuits through the new API #79.

##### Bug Fixes¶
• Fixed incorrect parsing of some API hub URLs #77.

• Fixed noise model handling for remote simulators #84.

### Qiskit 0.9¶

#### Terra 0.8¶

##### Highlights¶
• Introduction of the Pulse module under qiskit.pulse, which includes tools for building pulse commands, scheduling them on pulse channels, visualization, and running them on IBM Q devices.

• Improved QuantumCircuit and Instruction classes, allowing for the composition of arbitrary sub-circuits into larger circuits, and also for creating parametrized circuits.

• A powerful Quantum Info module under qiskit.quantum_info, providing tools to work with operators and channels and to use them inside circuits.

##### New Features¶
• The core StochasticSwap routine is implemented in Cython.

• Added QuantumChannel classes for manipulating quantum channels and CPTP maps.

• Support for parameterized circuits.

• The PassManager interface has been improved and new functions added for easier interaction and usage with custom pass managers.

• Preset PassManagers are now included which offer a predetermined pipeline of transpiler passes.

• User configuration files to let local environments override default values for some functions.

• New transpiler passes: EnlargeWithAncilla, Unroll2Q, NoiseAdaptiveLayout, OptimizeSwapBeforeMeasure, RemoveDiagonalGatesBeforeMeasure, CommutativeCancellation, Collect2qBlocks, and ConsolidateBlocks.

##### Compatibility Considerations¶

As part of the 0.8 release the following things have been deprecated and will either be removed or changed in a backwards incompatible manner in a future release. While not strictly necessary these are things to adjust for before the 0.9 (unless otherwise noted) release to avoid a breaking change in the future.

• The methods prefixed by _get in the DAGCircuit object are being renamed without that prefix.

• Changed elements in couplinglist of CouplingMap from tuples to lists.

• Unroller bases must now be explicit, and violation raises an informative QiskitError.

• The qiskit.tools.qcvv package is deprecated and will be removed in the in the future. You should migrate to using the Qiskit Ignis which replaces this module.

• The qiskit.compile() function is now deprecated in favor of explicitly using the qiskit.compiler.transpile() function to transform a circuit, followed by qiskit.compiler.assemble() to make a Qobj out of it. Instead of compile(...), use assemble(transpile(...), ...).

• qiskit.converters.qobj_to_circuits() has been deprecated and will be removed in a future release. Instead qiskit.assembler.disassemble() should be used to extract QuantumCircuit objects from a compiled Qobj.

• The qiskit.mapper namespace has been deprecated. The Layout and CouplingMap classes can be accessed via qiskit.transpiler.

• A few functions in qiskit.tools.qi.qi have been deprecated and moved to qiskit.quantum_info.

Please note that some backwards incompatible changes have been made during this release. The following notes contain information on how to adapt to these changes.

###### IBM Q Provider¶

The IBM Q provider was previously included in Terra, but it has been split out into a separate package qiskit-ibmq-provider. This will need to be installed, either via pypi with pip install qiskit-ibmq-provider or from source in order to access qiskit.IBMQ or qiskit.providers.ibmq. If you install qiskit with pip install qiskit, that will automatically install all subpackages of the Qiskit project.

###### Cython Components¶

Starting in the 0.8 release the core stochastic swap routine is now implemented in Cython. This was done to significantly improve the performance of the swapper, however if you build Terra from source or run on a non-x86 or other platform without prebuilt wheels and install from source distribution you’ll need to make sure that you have Cython installed prior to installing/building Qiskit Terra. This can easily be done with pip/pypi: pip install Cython.

###### Compiler Workflow¶

The qiskit.compile() function has been deprecated and replaced by first calling qiskit.compiler.transpile() to run optimization and mapping on a circuit, and then qiskit.compiler.assemble() to build a Qobj from that optimized circuit to send to a backend. While this is only a deprecation it will emit a warning if you use the old qiskit.compile() call.

transpile(), assemble(), execute() parameters

These functions are heavily overloaded and accept a wide range of inputs. They can handle circuit and pulse inputs. All kwargs except for backend for these functions now also accept lists of the previously accepted types. The initial_layout kwarg can now be supplied as a both a list and dictionary, e.g. to map a Bell experiment on qubits 13 and 14, you can supply: initial_layout=[13, 14] or initial_layout={qr[0]: 13, qr[1]: 14}

###### Qobj¶

The Qobj class has been split into two separate subclasses depending on the use case, either PulseQobj or QasmQobj for pulse and circuit jobs respectively. If you’re interacting with Qobj directly you may need to adjust your usage accordingly.

The qiskit.qobj.qobj_to_dict() is removed. Instead use the to_dict() method of a Qobj object.

###### Visualization¶

The largest change to the visualization module is it has moved from qiskit.tools.visualization to qiskit.visualization. This was done to indicate that the visualization module is more than just a tool. However, since this interface was declared stable in the 0.7 release the public interface off of qiskit.tools.visualization will continue to work. That may change in a future release, but it will be deprecated prior to removal if that happens.

The previously deprecated functions, plot_circuit(), latex_circuit_drawer(), generate_latex_source(), and matplotlib_circuit_drawer() from qiskit.tools.visualization have been removed. Instead of these functions, calling qiskit.visualization.circuit_drawer() with the appropriate arguments should be used.

The previously deprecated plot_barriers and reverse_bits keys in the style kwarg dictionary are deprecated, instead the qiskit.visualization.circuit_drawer() kwargs plot_barriers and reverse_bits should be used.

The Wigner plotting functions plot_wigner_function, plot_wigner_curve, plot_wigner_plaquette, and plot_wigner_data previously in the qiskit.tools.visualization._state_visualization module have been removed. They were never exposed through the public stable interface and were not well documented. The code to use this feature can still be accessed through the qiskit-tutorials repository.

###### Mapper¶

The public api from qiskit.mapper has been moved into qiskit.transpiler. While it has only been deprecated in this release, it will be removed in the 0.9 release so updating your usage of Layout and CouplingMap to import from qiskit.transpiler instead of qiskit.mapper before that takes place will avoid any surprises in the future.

#### Aer 0.2¶

##### New Features¶

• Added remap_noise_model function to noise.utils #181

• Added __eq__ method to NoiseModel, QuantumError, ReadoutError #181

• Added support for labelled gates in noise models #175

• Added optimized mcx, mcy, mcz, mcu1, mcu2, mcu3, gates to QubitVector #124

• Added optimized controlled-swap gate to QubitVector #142

• Added gate-fusion optimization for QasmController, which is enabled by setting fusion_enable=true #136

• Added better management of failed simulations #167

• Added qubits truncate optimization for unused qubits #164

• Added ability to disable depolarizing error on device noise model #131

• Added initialize simulator instruction to statevector_state #117, #137

• Added coupling maps to simulators #93

• Added circuit optimization framework #83

• Added wheels support for Debian-like distributions #69

• Added Simulation method based on Stabilizer Rank Decompositions #51

• Added basis_gates kwarg to NoiseModel init #175.

• Added an optional parameter to NoiseModel.as_dict() for returning dictionaries that can be serialized using the standard json library directly #165

• Improve noise transformations #162

• Improve error reporting #160

• Improve efficiency of parallelization with max_memory_mb a new parameter of backend_opts #61

• Improve u1 performance in statevector #123

##### Bug Fixes¶
• Fixed OpenMP clashing problems on macOS for the Terra add-on #46

##### Compatibility Considerations¶
• Deprecated "initial_statevector" backend option for QasmSimulator and StatevectorSimulator #185

• Renamed "chop_threshold" backend option to "zero_threshold" and changed default value to 1e-10 #185

#### Ignis 0.1¶

##### New Features¶
• Quantum volume

• Measurement mitigation using tensored calibrations

• Simultaneous RB has the option to align Clifford gates across subsets

• Measurement correction can produce a new calibration for a subset of qubits

##### Compatibility Considerations¶
• RB writes to the minimal set of classical registers (it used to be Q[i]->C[i]). This change enables measurement correction with RB. Unless users had external analysis code, this will not change outcomes. RB circuits from 0.1 are not compatible with 0.1.1 fitters.

#### Aqua 0.5¶

##### New Features¶
• Implementation of the HHL algorithm supporting LinearSystemInput

• Pluggable component Eigenvalues with variant EigQPE

• Pluggable component Reciprocal with variants LookupRotation and LongDivision

• Multiple-Controlled U1 and U3 operations mcu1 and mcu3

• Pluggable component QFT derived from component IQFT

• Summarized the transpiled circuits at the DEBUG logging level

• QuantumInstance accepts basis_gates and coupling_map again.

• Support to use cx gate for the entanglement in RY and RYRZ variational form (cz is the default choice)

• Support to use arbitrary mixer Hamiltonian in QAOA, allowing use of QAOA in constrained optimization problems [arXiv:1709.03489]

• Added variational algorithm base class VQAlgorithm, implemented by VQE and QSVMVariational

• Added ising/docplex.py for automatically generating Ising Hamiltonian from optimization models of DOcplex

• Added 'basic-dirty-ancilla’ mode for mct

• Added mcmt for Multi-Controlled, Multi-Target gate

• Exposed capabilities to generate circuits from logical AND, OR, DNF (disjunctive normal forms), and CNF (conjunctive normal forms) formulae

• Added the capability to generate circuits from ESOP (exclusive sum of products) formulae with optional optimization based on Quine-McCluskey and ExactCover

• Added LogicalExpressionOracle for generating oracle circuits from arbitrary Boolean logic expressions (including DIMACS support) with optional optimization capability

• Added TruthTableOracle for generating oracle circuits from truth-tables with optional optimization capability

• Added CustomCircuitOracle for generating oracle from user specified circuits

• Added implementation of the Deutsch-Jozsa algorithm

• Added implementation of the Bernstein-Vazirani algorithm

• Added implementation of the Simon’s algorithm

• Added implementation of the Shor’s algorithm

• Added optional capability for Grover’s algorithm to take a custom initial state (as opposed to the default uniform superposition)

• Added capability to create a Custom initial state using existing circuit

• Multivariate distributions added, so uncertainty models now have univariate and multivariate distribution components

• Added option to include or skip the swaps operations for qft and iqft circuit constructions

• Added classical linear system solver ExactLSsolver

• Added parameters auto_hermitian and auto_resize to HHL algorithm to support non-Hermitian and non $$2^n$$ sized matrices by default

• Added another feature map, RawFeatureVector, that directly maps feature vectors to qubits’ states for classification

• SVM_Classical can now load models trained by QSVM

##### Bug Fixes¶
• Fixed ising/docplex.py to correctly multiply constant values in constraints

• Fixed package setup to correctly identify namespace packages using setuptools.find_namespace_packages

##### Compatibility Considerations¶
• QuantumInstance does not take memory anymore.

• Moved command line and GUI to separate repo (qiskit_aqua_uis)

• Removed the SAT-specific oracle (now supported by LogicalExpressionOracle)

• Changed advanced mode implementation of mct: using simple h gates instead of ch, and fixing the old recursion step in _multicx

• Components random_distributions renamed to uncertainty_models

• Reorganized the constructions of various common gates (ch, cry, mcry, mct, mcu1, mcu3, mcmt, logic_and, and logic_or) and circuits (PhaseEstimationCircuit, BooleanLogicCircuits, FourierTransformCircuits, and StateVectorCircuits) under the circuits directory

• Renamed the algorithm QSVMVariational to VQC, which stands for Variational Quantum Classifier

• Renamed the algorithm QSVMKernel to QSVM

• Renamed the class SVMInput to ClassificationInput

• Renamed problem type 'svm_classification' to 'classification'

• Changed the type of entangler_map used in FeatureMap and VariationalForm to list of lists

#### IBM Q Provider 0.1¶

##### New Features¶
• This is the first release as a standalone package. If you are installing Terra standalone you’ll also need to install the qiskit-ibmq-provider package with pip install qiskit-ibmq-provider if you want to use the IBM Q backends.

• Support for non-Qobj format jobs has been removed from the provider. You’ll have to convert submissions in an older format to Qobj before you can submit.

### Qiskit 0.8¶

In Qiskit 0.8 we introduced the Qiskit Ignis element. It also includes the Qiskit Terra element 0.7.1 release which contains a bug fix for the BasicAer Python simulator.

No Change

No Change

#### Ignis 0.1¶

This is the first release of Qiskit Ignis.

### Qiskit 0.7¶

In Qiskit 0.7 we introduced Qiskit Aer and combined it with Qiskit Terra.

#### Terra 0.7¶

##### New Features¶

This release includes several new features and many bug fixes. With this release the interfaces for circuit diagram, histogram, bloch vectors, and state visualizations are declared stable. Additionally, this release includes a defined and standardized bit order/endianness throughout all aspects of Qiskit. These are all declared as stable interfaces in this release which won’t have breaking changes made moving forward, unless there is appropriate and lengthy deprecation periods warning of any coming changes.

There is also the introduction of the following new features:

• A new ASCII art circuit drawing output mode

• A new circuit drawing interface off of QuantumCircuit objects that enables calls of circuit.draw() or print(circuit) to render a drawing of circuits

• A visualizer for drawing the DAG representation of a circuit

• A new quantum state plot type for hinton diagrams in the local matplotlib based state plots

• 2 new constructor methods off the QuantumCircuit class from_qasm_str() and from_qasm_file() which let you easily create a circuit object from OpenQASM

• A new function plot_bloch_multivector() to plot Bloch vectors from a tensored state vector or density matrix

• Per-shot measurement results are available in simulators and select devices. These can be accessed by setting the memory kwarg to True when calling compile() or execute() and then accessed using the get_memory() method on the Result object.

• A qiskit.quantum_info module with revamped Pauli objects and methods for working with quantum states

• New transpile passes for circuit analysis and transformation: CommutationAnalysis, CommutationTransformation, CXCancellation, Decompose, Unroll, Optimize1QGates, CheckMap, CXDirection, BarrierBeforeFinalMeasurements

• New alternative swap mapper passes in the transpiler: BasicSwap, LookaheadSwap, StochasticSwap

• More advanced transpiler infrastructure with support for analysis passes, transformation passes, a global property_set for the pass manager, and repeat-until control of passes

##### Compatibility Considerations¶

As part of the 0.7 release the following things have been deprecated and will either be removed or changed in a backwards incompatible manner in a future release. While not strictly necessary these are things to adjust for before the next release to avoid a breaking change.

• plot_circuit(), latex_circuit_drawer(), generate_latex_source(), and matplotlib_circuit_drawer() from qiskit.tools.visualization are deprecated. Instead the circuit_drawer() function from the same module should be used, there are kwarg options to mirror the functionality of all the deprecated functions.

• The current default output of circuit_drawer() (using latex and falling back on python) is deprecated and will be changed to just use the text output by default in future releases.

• The qiskit.wrapper.load_qasm_string() and qiskit.wrapper.load_qasm_file() functions are deprecated and the QuantumCircuit.from_qasm_str() and QuantumCircuit.from_qasm_file() constructor methods should be used instead.

• The plot_barriers and reverse_bits keys in the style kwarg dictionary are deprecated, instead the qiskit.tools.visualization.circuit_drawer() kwargs plot_barriers and reverse_bits should be used instead.

• The functions plot_state() and iplot_state() have been depreciated. Instead the functions plot_state_*() and iplot_state_*() should be called for the visualization method required.

• The skip_transpiler argumentt has been deprecated from compile() and execute(). Instead you can use the PassManager directly, just set the pass_manager to a blank PassManager object with PassManager()

• The transpile_dag() function format kwarg for emitting different output formats is deprecated, instead you should convert the default output DAGCircuit object to the desired format.

• The unrollers have been deprecated, moving forward only DAG to DAG unrolling will be supported.

Please note that some backwards-incompatible changes have been made during this release. The following notes contain information on how to adapt to these changes.

###### Changes to Result objects¶

As part of the rewrite of the Results object to be more consistent and a stable interface moving forward a few changes have been made to how you access the data stored in the result object. First the get_data() method has been renamed to just data(). Accompanying that change is a change in the data format returned by the function. It is now returning the raw data from the backends instead of doing any post-processing. For example, in previous versions you could call:

result = execute(circuit, backend).result()
unitary = result.get_data()['unitary']
print(unitary)


and that would return the unitary matrix like:

[[1+0j, 0+0.5j], [0-0.5j][-1+0j]]


But now if you call (with the renamed method):

result.data()['unitary']


it will return something like:

[[[1, 0], [0, -0.5]], [[0, -0.5], [-1, 0]]]


To get the post processed results in the same format as before the 0.7 release you must use the get_counts(), get_statevector(), and get_unitary() methods on the result object instead of get_data()['counts'], get_data()['statevector'], and get_data()['unitary'] respectively.

Additionally, support for len() and indexing on a Result object has been removed. Instead you should deal with the output from the post processed methods on the Result objects.

Also, the get_snapshot() and get_snapshots() methods from the Result class have been removed. Instead you can access the snapshots using Result.data()['snapshots'].

###### Changes to Visualization¶

The largest change made to visualization in the 0.7 release is the removal of Matplotlib and other visualization dependencies from the project requirements. This was done to simplify the requirements and configuration required for installing Qiskit. If you plan to use any visualizations (including all the jupyter magics) except for the text, latex, and latex_source output for the circuit drawer you’ll you must manually ensure that the visualization dependencies are installed. You can leverage the optional requirements to the Qiskit Terra package to do this:

pip install qiskit-terra[visualization]


Aside from this there have been changes made to several of the interfaces as part of the stabilization which may have an impact on existing code. The first is the basis kwarg in the circuit_drawer() function is no longer accepted. If you were relying on the circuit_drawer() to adjust the basis gates used in drawing a circuit diagram you will have to do this priort to calling circuit_drawer(). For example:

from qiskit.tools import visualization
visualization.circuit_drawer(circuit, basis_gates='x,U,CX')


will have to be adjust to be:

from qiskit import BasicAer
from qiskit import transpiler
from qiskit.tools import visualization
backend = BasicAer.backend('qasm_simulator')
draw_circ = transpiler.transpile(circuit, backend, basis_gates='x,U,CX')
visualization.circuit_drawer(draw_circ)


Moving forward the circuit_drawer() function will be the sole interface for circuit drawing in the visualization module. Prior to the 0.7 release there were several other functions which either used different output backends or changed the output for drawing circuits. However, all those other functions have been deprecated and that functionality has been integrated as options on circuit_drawer().

For the other visualization functions, plot_histogram() and plot_state() there are also a few changes to check when upgrading. First is the output from these functions has changed, in prior releases these would interactively show the output visualization. However that has changed to instead return a matplotlib.Figure object. This provides much more flexibility and options to interact with the visualization prior to saving or showing it. This will require adjustment to how these functions are consumed. For example, prior to this release when calling:

plot_histogram(counts)
plot_state(rho)


would open up new windows (depending on matplotlib backend) to display the visualization. However starting in the 0.7 you’ll have to call show() on the output to mirror this behavior. For example:

plot_histogram(counts).show()
plot_state(rho).show()


or:

hist_fig = plot_histogram(counts)
state_fig = plot_state(rho)
hist_fig.show()
state_fig.show()


Note that this is only for when running outside of Jupyter. No adjustment is required inside a Jupyter environment because Jupyter notebooks natively understand how to render matplotlib.Figure objects.

However, returning the Figure object provides additional flexibility for dealing with the output. For example instead of just showing the figure you can now directly save it to a file by leveraging the savefig() method. For example:

hist_fig = plot_histogram(counts)
state_fig = plot_state(rho)
hist_fig.savefig('histogram.png')
state_fig.savefig('state_plot.png')


The other key aspect which has changed with these functions is when running under jupyter. In the 0.6 release plot_state() and plot_histogram() when running under jupyter the default behavior was to use the interactive Javascript plots if the externally hosted Javascript library for rendering the visualization was reachable over the network. If not it would just use the matplotlib version. However in the 0.7 release this no longer the case, and separate functions for the interactive plots, iplot_state() and iplot_histogram() are to be used instead. plot_state() and plot_histogram() always use the matplotlib versions.

Additionally, starting in this release the plot_state() function is deprecated in favor of calling individual methods for each method of plotting a quantum state. While the plot_state() function will continue to work until the 0.9 release, it will emit a warning each time it is used. The

Qiskit Terra 0.6

Qiskit Terra 0.7+

plot_state(rho)

plot_state_city(rho)

plot_state(rho, method=’city’)

plot_state_city(rho)

plot_state(rho, method=’paulivec’)

plot_state_paulivec(rho)

plot_state(rho, method=’qsphere’)

plot_state_qsphere(rho)

plot_state(rho, method=’bloch’)

plot_bloch_multivector(rho)

plot_state(rho, method=’hinton’)

plot_state_hinton(rho)

The same is true for the interactive JS equivalent, iplot_state(). The function names are all the same, just with a prepended i for each function. For example, iplot_state(rho, method='paulivec') is iplot_state_paulivec(rho).

###### Changes to Backends¶

With the improvements made in the 0.7 release there are a few things related to backends to keep in mind when upgrading. The biggest change is the restructuring of the provider instances in the root qiskit namespace. The Aer provider is not installed by default and requires the installation of the qiskit-aer package. This package contains the new high performance fully featured simulator. If you installed via pip install qiskit you’ll already have this installed. The python simulators are now available under qiskit.BasicAer and the old C++ simulators are available with qiskit.LegacySimulators. This also means that the implicit fallback to python based simulators when the C++ simulators are not found doesn’t exist anymore. If you ask for a local C++ based simulator backend, and it can’t be found an exception will be raised instead of just using the python simulator instead.

Additionally the previously deprecation top level functions register() and available_backends() have been removed. Also, the deprecated backend.parameters() and backend.calibration() methods have been removed in favor of backend.properties(). You can refer to the 0.6 release notes section Working with backends for more details on these changes.

The backend.jobs() and backend.retrieve_jobs() calls no longer return results from those jobs. Instead you must call the result() method on the returned jobs objects.

###### Changes to the compiler, transpiler, and unrollers¶

As part of an effort to stabilize the compiler interfaces there have been several changes to be aware of when leveraging the compiler functions. First it is important to note that the qiskit.transpiler.transpile() function now takes a QuantumCircuit object (or a list of them) and returns a QuantumCircuit object (or a list of them). The DAG processing is done internally now.

You can also easily switch between circuits, DAGs, and Qobj now using the functions in qiskit.converters.

#### Aer 0.1¶

##### New Features¶

Aer provides three simulator backends:

• QasmSimulator: simulate experiments and return measurement outcomes

• StatevectorSimulator: return the final statevector for a quantum circuit acting on the all zero state

• UnitarySimulator: return the unitary matrix for a quantum circuit

noise module: contains advanced noise modeling features for the QasmSimulator

• NoiseModel, QuantumError, ReadoutError classes for simulating a Qiskit quantum circuit in the presence of errors

• errors submodule including functions for generating QuantumError objects for the following types of quantum errors: Kraus, mixed unitary, coherent unitary, Pauli, depolarizing, thermal relaxation, amplitude damping, phase damping, combined phase and amplitude damping

• device submodule for automatically generating a noise model based on the BackendProperties of a device

utils module:

• qobj_utils provides functions for directly modifying a Qobj to insert special simulator instructions not yet supported through the Qiskit Terra API.

#### Aqua 0.4¶

##### New Features¶
• Programmatic APIs for algorithms and components – each component can now be instantiated and initialized via a single (non-empty) constructor call

• QuantumInstance API for algorithm/backend decoupling – QuantumInstance encapsulates a backend and its settings

• Updated documentation and Jupyter Notebooks illustrating the new programmatic APIs

• Transparent parallelization for gradient-based optimizers

• Multiple-Controlled-NOT (cnx) operation

• Pluggable algorithmic component RandomDistribution

• Concrete implementations of RandomDistribution: BernoulliDistribution, LogNormalDistribution, MultivariateDistribution, MultivariateNormalDistribution, MultivariateUniformDistribution, NormalDistribution, UniformDistribution, and UnivariateDistribution

• Concrete implementations of UncertaintyProblem: FixedIncomeExpectedValue, EuropeanCallExpectedValue, and EuropeanCallDelta

• Amplitude Estimation algorithm

• Qiskit Optimization: New Ising models for optimization problems exact cover, set packing, vertex cover, clique, and graph partition

• Qiskit AI:

• New feature maps extending the FeatureMap pluggable interface: PauliExpansion and PauliZExpansion

• Training model serialization/deserialization mechanism

• Qiskit Finance:

• Amplitude estimation for Bernoulli random variable: illustration of amplitude estimation on a single qubit problem

• European call option: expected value and delta (using univariate distributions)

• Fixed income asset pricing: expected value (using multivariate distributions)

• The Pauli string in Operator class is aligned with Terra 0.7. Now the order of a n-qubit pauli string is q_{n-1}...q{0} Thus, the (de)serialier (save_to_dict and load_from_dict) in the Operator class are also changed to adopt the changes of Pauli class.

##### Compatibility Considerations¶
• HartreeFock component of pluggable type InitialState moved to Qiskit Chemistry

• UCCSD component of pluggable type VariationalForm moved to Qiskit Chemistry

### Qiskit 0.6¶

#### Terra 0.6¶

##### Highlights¶

This release includes a redesign of internal components centered around a new, formal communication format (Qobj), along with long awaited features to improve the user experience as a whole. The highlights, compared to the 0.5 release, are:

• Improvements for inter-operability (based on the Qobj specification) and extensibility (facilities for extending Qiskit with new backends in a seamless way)

• New options for handling credentials and authentication for the IBM Q backends, aimed at simplifying the process and supporting automatic loading of user credentials

• A revamp of the visualization utilities: stylish interactive visualizations are now available for Jupyter users, along with refinements for the circuit drawer (including a matplotlib-based version)

• Performance improvements centered around circuit transpilation: the basis for a more flexible and modular architecture have been set, including parallelization of the circuit compilation and numerous optimizations

##### Compatibility Considerations¶

Please note that some backwards-incompatible changes have been introduced during this release – the following notes contain information on how to adapt to the new changes.

###### Removal of QuantumProgram¶

As hinted during the 0.5 release, the deprecation of the QuantumProgram class has now been completed and is no longer available, in favor of working with the individual components (BaseJob, QuantumCircuit, ClassicalRegister, QuantumRegister, qiskit) directly.

Please check the 0.5 release notes and the examples for details about the transition:

from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import Aer, execute

q = QuantumRegister(2)
c = ClassicalRegister(2)
qc = QuantumCircuit(q, c)

qc.h(q[0])
qc.cx(q[0], q[1])
qc.measure(q, c)

backend = get_backend('qasm_simulator')

job_sim = execute(qc, backend)
sim_result = job_sim.result()

print("simulation: ", sim_result)
print(sim_result.get_counts(qc))

###### IBM Q Authentication and Qconfig.py¶

The managing of credentials for authenticating when using the IBM Q backends has been expanded, and there are new options that can be used for convenience:

1. save your credentials in disk once, and automatically load them in future sessions. This provides a one-off mechanism:

from qiskit import IBMQ
IBQM.save_account('MY_API_TOKEN', 'MY_API_URL')


afterwards, your credentials can be automatically loaded from disk by invoking load_accounts():

from qiskit import IBMQ


or you can load only specific accounts if you only want to use those in a session:

IBMQ.load_accounts(project='MY_PROJECT')

2. use environment variables. If QE_TOKEN and QE_URL is set, the IBMQ.load_accounts() call will automatically load the credentials from them.

Additionally, the previous method of having a Qconfig.py file in the program folder and passing the credentials explicitly is still supported.

###### Working with backends¶

A new mechanism has been introduced in Terra 0.6 as the recommended way for obtaining a backend, allowing for more powerful and unified filtering and integrated with the new credentials system. The previous top-level methods register(), available_backends() and get_backend() are still supported, but will deprecated in upcoming versions in favor of using the qiskit.IBMQ and qiskit.Aer objects directly, which allow for more complex filtering.

For example, to list and use a local backend:

from qiskit import Aer

all_local_backends = Aer.backends(local=True)  # returns a list of instances
qasm_simulator = Aer.backends('qasm_simulator')


And for listing and using remote backends:

from qiskit import IBMQ

IBMQ.enable_account('MY_API_TOKEN')
5_qubit_devices = IBMQ.backends(simulator=True, n_qubits=5)
ibmqx4 = IBMQ.get_backend('ibmqx4')


Please note as well that the names of the local simulators have been simplified. The previous names can still be used, but it is encouraged to use the new, shorter names:

Qiskit Terra 0.5

Qiskit Terra 0.6

‘local_qasm_simulator’

‘qasm_simulator’

‘local_statevector_simulator’

‘statevector_simulator’

‘local_unitary_simulator_py’

‘unitary_simulator’

###### Backend and Job API changes¶
• Jobs submitted to IBM Q backends have improved capabilities. It is possible to cancel them and replenish credits (job.cancel()), and to retrieve previous jobs executed on a specific backend either by job id (backend.retrieve_job(job_id)) or in batch of latest jobs (backend.jobs(limit))

• Properties for checking each individual job status (queued, running, validating, done and cancelled) no longer exist. If you want to check the job status, use the identity comparison against job.status:

from qiskit.backends import JobStatus

job = execute(circuit, backend)
if job.status() is JobStatus.RUNNING:
handle_job(job)


Please consult the new documentation of the IBMQJob class to get further insight in how to use the simplified API.

• A number of members of BaseBackend and BaseJob are no longer properties, but methods, and as a result they need to be invoked as functions.

Qiskit Terra 0.5

Qiskit Terra 0.6

backend.name

backend.name()

backend.status

backend.status()

backend.configuration

backend.configuration()

backend.calibration

backend.properties()

backend.parameters

backend.jobs() backend.retrieve_job(job_id)

job.status

job.status()

job.cancelled

job.queue_position()

job.running

job.cancel()

job.queued

job.done

###### Better Jupyter tools¶

The new release contains improvements to the user experience while using Jupyter notebooks.

First, new interactive visualizations of counts histograms and quantum states are provided: plot_histogram() and plot_state(). These methods will default to the new interactive kind when the environment is Jupyter and internet connection exists.

Secondly, the new release provides Jupyter cell magics for keeping track of the progress of your code. Use %%qiskit_job_status to keep track of the status of submitted jobs to IBM Q backends. Use %%qiskit_progress_bar to keep track of the progress of compilation/execution.

### Qiskit 0.5¶

#### Terra 0.5¶

##### Highlights¶

This release brings a number of improvements to Qiskit, both for the user experience and under the hood. Please refer to the full changelog for a detailed description of the changes - the highlights are:

• new statevector simulators and feature and performance improvements to the existing ones (in particular to the C++ simulator), along with a reorganization of how to work with backends focused on extensibility and flexibility (using aliases and backend providers)

• reorganization of the asynchronous features, providing a friendlier interface for running jobs asynchronously via Job instances

• numerous improvements and fixes throughout the Terra as a whole, both for convenience of the users (such as allowing anonymous registers) and for enhanced functionality (such as improved plotting of circuits)

##### Compatibility Considerations¶

Please note that several backwards-incompatible changes have been introduced during this release as a result of the ongoing development. While some of these features will continue to be supported during a period of time before being fully deprecated, it is recommended to update your programs in order to prepare for the new versions and take advantage of the new functionality.

###### QuantumProgram changes¶

Several methods of the QuantumProgram class are on their way to being deprecated:

• methods for interacting with the backends and the API:

The recommended way for opening a connection to the IBM Q API and for using the backends is through the top-level functions directly instead of the QuantumProgram methods. In particular, the qiskit.register() method provides the equivalent of the previous qiskit.QuantumProgram.set_api() call. In a similar vein, there is a new qiskit.available_backends(), qiskit.get_backend() and related functions for querying the available backends directly. For example, the following snippet for version 0.4:

from qiskit import QuantumProgram

quantum_program = QuantumProgram()
quantum_program.set_api(token, url)
backends = quantum_program.available_backends()
print(quantum_program.get_backend_status('ibmqx4')


would be equivalent to the following snippet for version 0.5:

from qiskit import register, available_backends, get_backend

register(token, url)
backends = available_backends()
backend = get_backend('ibmqx4')
print(backend.status)

• methods for compiling and executing programs:

The top-level functions now also provide equivalents for the qiskit.QuantumProgram.compile() and qiskit.QuantumProgram.execute() methods. For example, the following snippet from version 0.4:

quantum_program.execute(circuit, args, ...)


would be equivalent to the following snippet for version 0.5:

from qiskit import execute

execute(circuit, args, ...)


In general, from version 0.5 onwards we encourage to try to make use of the individual objects and classes directly instead of relying on QuantumProgram. For example, a QuantumCircuit can be instantiated and constructed by appending QuantumRegister, ClassicalRegister, and gates directly. Please check the update example in the Quickstart section, or the using_qiskit_core_level_0.py and using_qiskit_core_level_1.py examples on the main repository.

###### Backend name changes¶

In order to provide a more extensible framework for backends, there have been some design changes accordingly:

• local simulator names

The names of the local simulators have been homogenized in order to follow the same pattern: PROVIDERNAME_TYPE_simulator_LANGUAGEORPROJECT - for example, the C++ simulator previously named local_qiskit_simulator is now local_qasm_simulator_cpp. An overview of the current simulators:

• QASM simulator is supposed to be like an experiment. You apply a circuit on some qubits, and observe measurement results - and you repeat for many shots to get a histogram of counts via result.get_counts().

• Statevector simulator is to get the full statevector ($$2^n$$ amplitudes) after evolving the zero state through the circuit, and can be obtained via result.get_statevector().

• Unitary simulator is to get the unitary matrix equivalent of the circuit, returned via result.get_unitary().

• In addition, you can get intermediate states from a simulator by applying a snapshot(slot) instruction at various spots in the circuit. This will save the current state of the simulator in a given slot, which can later be retrieved via result.get_snapshot(slot).

• backend aliases:

The SDK now provides an “alias” system that allows for automatically using the most performant simulator of a specific type, if it is available in your system. For example, with the following snippet:

from qiskit import get_backend

backend = get_backend('local_statevector_simulator')


the backend will be the C++ statevector simulator if available, falling back to the Python statevector simulator if not present.

###### More flexible names and parameters¶

Several functions of the SDK have been made more flexible and user-friendly:

• automatic circuit and register names

qiskit.ClassicalRegister, qiskit.QuantumRegister and qiskit.QuantumCircuit can now be instantiated without explicitly giving them a name - a new autonaming feature will automatically assign them an identifier:

q = QuantumRegister(2)


Please note as well that the order of the parameters have been swapped QuantumRegister(size, name).

• methods accepting names or instances

In combination with the autonaming changes, several methods such as qiskit.Result.get_data() now accept both names and instances for convenience. For example, when retrieving the results for a job that has a single circuit such as:

qc = QuantumCircuit(..., name='my_circuit')
job = execute(qc, ...)
result = job.result()


The following calls are equivalent:

data = result.get_data('my_circuit')
data = result.get_data(qc)
data = result.get_data()