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Qiskit 0.25 release notes


0.25.4

Terra 0.17.2

Prelude

This is a bugfix release that fixes several issues from the 0.17.1 release. Most importantly this release fixes compatibility for the QuantumInstance class when running on backends that are based on the BackendV1 abstract class. This fixes all the algorithms and applications built on qiskit.algorithms or qiskit.opflow when running on newer backends.

Bug Fixes

Aer 0.8.2

No change

Ignis 0.6.0

No change

Aqua 0.9.1

No change

IBM Q Provider 0.12.3

No change


0.25.3

Terra 0.17.1

No change

Aer 0.8.2

Known Issues

  • The SaveExpectationValue and SaveExpectationValueVariance have been disabled for the extended_stabilizer method of the QasmSimulator and AerSimulator due to returning the incorrect value for certain Pauli operator components. Refer to #1227 <https://github.com/Qiskit/qiskit-aer/issues/1227(opens in a new tab)> for more information and examples.

Bug Fixes

  • Fixes performance issue with how the basis_gates configuration attribute was set. Previously there were unintended side-effects to the backend class which could cause repeated simulation runtime to incrementally increase. Refer to #1229 <https://github.com/Qiskit/qiskit-aer/issues/1229(opens in a new tab)> for more information and examples.
  • Fixes a bug with the "multiplexer" simulator instruction where the order of target and control qubits was reversed to the order in the Qiskit instruction.
  • Fixes a bug introduced in 0.8.0 where GPU simulations would allocate unneeded host memory in addition to the GPU memory.
  • Fixes a bug in the stabilizer simulator method of the QasmSimulator and AerSimulator where the expectation value for the save_expectation_value and snapshot_expectation_value could have the wrong sign for certain Y Pauli’s.

Ignis 0.6.0

No change

Aqua 0.9.1

No change

IBM Q Provider 0.12.3

No change


0.25.2

Terra 0.17.1

No change

Aer 0.8.1

No change

Ignis 0.6.0

No change

Aqua 0.9.1

No change

IBM Q Provider 0.12.3

Other Notes

  • The qiskit.providers.ibmq.experiment.analysis_result.AnalysisResult fit attribute is now optional.

0.25.1

Terra 0.17.1

Prelude

This is a bugfix release that fixes several issues from the 0.17.0 release. Most importantly this release fixes the incorrectly constructed sdist package for the 0.17.0 release which was not actually buildable and was blocking installation on platforms without precompiled binaries available.

Bug Fixes

  • Fixed an issue where the global_phase attribute would not be preserved in the output QuantumCircuit object when the qiskit.circuit.QuantumCircuit.reverse_bits() method was called. For example:

    import math
    from qiskit import QuantumCircuit
     
    qc = QuantumCircuit(3, 2, global_phase=math.pi)
    qc.h(0)
    qc.s(1)
    qc.cx(0, 1)
    qc.measure(0, 1)
    qc.x(0)
    qc.y(1)
     
    reversed = qc.reverse_bits()
    print(reversed.global_phase)

    will now correctly print π\pi.

  • Fixed an issue where the transpiler pass Unroller didn’t preserve global phase in case of nested instructions with one rule in their definition. Fixed #6134(opens in a new tab)

  • Fixed an issue where the parameter attribute of a ControlledGate object built from a UnitaryGate was not being set to the unitary matrix of the UnitaryGate object. Previously, control() was building a ControlledGate with the parameter attribute set to the controlled version of UnitaryGate matrix. This would lead to a modification of the parameter of the base UnitaryGate object and subsequent calls to inverse() was creating the inverse of a double-controlled UnitaryGate. Fixed #5750(opens in a new tab)

  • Fixed an issue with the preset pass managers level_0_pass_manager and level_1_pass_manager (which corresponds to optimization_level 0 and 1 for transpile()) where in some cases they would produce circuits not in the requested basis.

  • Fix a bug where using SPSA with automatic calibration of the learning rate and perturbation (i.e. learning_rate and perturbation are None in the initializer), stores the calibration for all future optimizations. Instead, the calibration should be done for each new objective function.

Aer 0.8.1

Bug Fixes

  • Fixed an issue with use of the matrix_product_state method of the AerSimulator and QasmSimulator simulators when running a noisy simulation with Kraus errors. Previously, the matrix product state simulation method would not propogate changes to neighboring qubits after applying the Kraus matrix. This has been fixed so the output from the simulation is correct. Fixed #1184(opens in a new tab) and #1205(opens in a new tab)
  • Fixed an issue where the qiskit.extensions.Initialize instruction would disable measurement sampling optimization for the statevector and matrix_product_state simulation methods of the AerSimulator and QasmSimulator simulators, even when it was the first circuit instruction or applied to all qubits and hence deterministic. Fixed #1210(opens in a new tab)
  • Fix an issue with the SaveStatevector and SnapshotStatevector instructions when used with the extended_stabilizer simulation method of the AerSimulator and QasmSimulator simulators where it would return an unnormalized statevector. Fixed #1196(opens in a new tab)
  • The matrix_product_state simulation method now has support for it’s previously missing set state instruction, qiskit.providers.aer.library.SetMatrixProductState, which enables setting the state of a simulation in a circuit.

Ignis 0.6.0

No change

Aqua 0.9.1

IBM Q Provider 0.12.2

No change


0.25.0

This release officially deprecates the Qiskit Aqua project. Accordingly, in a future release the qiskit-aqua package will be removed from the Qiskit metapackage, which means in that future release pip install qiskit will no longer include qiskit-aqua. The application modules that are provided by qiskit-aqua have been split into several new packages: qiskit-optimization, qiskit-nature, qiskit-machine-learning, and qiskit-finance. These packages can be installed by themselves (via the standard pip install command, e.g. pip install qiskit-nature) or with the rest of the Qiskit metapackage as optional extras (e.g. pip install 'qiskit[finance,optimization]' or pip install 'qiskit[all]' The core algorithms and the operator flow now exist as part of qiskit-terra at qiskit.algorithms and qiskit.opflow. Depending on your existing usage of Aqua you should either use the application packages or the new modules in Qiskit Terra. For more details on how to migrate from Qiskit Aqua, you can refer to the migration guide(opens in a new tab).

Terra 0.17.0

Prelude

The Qiskit Terra 0.17.0 includes many new features and bug fixes. The major new feature for this release is the introduction of the qiskit.algorithms and qiskit.opflow modules which were migrated and adapted from the qiskit.aqua project.

New Features

  • The qiskit.pulse.call() function can now take a Parameter object along with a parameterized subroutine. This enables assigning different values to the Parameter objects for each subroutine call.

    For example,

    from qiskit.circuit import Parameter
    from qiskit import pulse
     
    amp = Parameter('amp')
     
    with pulse.build() as subroutine:
        pulse.play(pulse.Gaussian(160, amp, 40), DriveChannel(0))
     
    with pulse.build() as main_prog:
        pulse.call(subroutine, amp=0.1)
        pulse.call(subroutine, amp=0.3)
  • The qiskit.providers.models.QasmBackendConfiguration has a new field processor_type which can optionally be used to provide information about a backend’s processor in the form: {"family": <str>, "revision": <str>, segment: <str>}. For example: {"family": "Canary", "revision": "1.0", segment: "A"}.

  • The qiskit.pulse.Schedule, qiskit.pulse.Instruction, and qiskit.pulse.Channel classes now have a parameter property which will return any Parameter objects used in the object and a is_parameterized() method which will return True if any parameters are used in the object.

    For example:

    from qiskit.circuit import Parameter
    from qiskit import pulse
     
    shift = Parameter('alpha')
     
    schedule = pulse.Schedule()
    schedule += pulse.SetFrequency(shift, pulse.DriveChannel(0))
     
    assert schedule.is_parameterized() == True
    print(schedule.parameters)
  • Added a PiecewiseChebyshev to the qiskit.circuit.library for implementing a piecewise Chebyshev approximation of an input function. For a given function f(x)f(x) and degree dd, this class class implements a piecewise polynomial Chebyshev approximation on nn qubits to f(x)f(x) on the given intervals. All the polynomials in the approximation are of degree dd.

    For example:

    import numpy as np
    from qiskit import QuantumCircuit
    from qiskit.circuit.library.arithmetic.piecewise_chebyshev import PiecewiseChebyshev
    f_x, degree, breakpoints, num_state_qubits = lambda x: np.arcsin(1 / x), 2, [2, 4], 2
    pw_approximation = PiecewiseChebyshev(f_x, degree, breakpoints, num_state_qubits)
    pw_approximation._build()
    qc = QuantumCircuit(pw_approximation.num_qubits)
    qc.h(list(range(num_state_qubits)))
    qc.append(pw_approximation.to_instruction(), qc.qubits)
    qc.draw(output='mpl')
  • The BackendProperties class now has a readout_length() method, which returns the readout length [sec] of the given qubit.

  • A new class, ScheduleBlock, has been added to the qiskit.pulse module. This class provides a new representation of a pulse program. This representation is best suited for the pulse builder syntax and is based on relative instruction ordering.

    This representation takes alignment_context instead of specifying starting time t0 for each instruction. The start time of instruction is implicitly allocated with the specified transformation and relative position of instructions.

    The ScheduleBlock allows for lazy instruction scheduling, meaning we can assign arbitrary parameters to the duration of instructions.

    For example:

    from qiskit.pulse import ScheduleBlock, DriveChannel, Gaussian
    from qiskit.pulse.instructions import Play, Call
    from qiskit.pulse.transforms import AlignRight
    from qiskit.circuit import Parameter
     
    dur = Parameter('rabi_duration')
     
    block = ScheduleBlock(alignment_context=AlignRight())
    block += Play(Gaussian(dur, 0.1, dur/4), DriveChannel(0))
    block += Call(measure_sched)  # subroutine defined elsewhere

    this code defines an experiment scanning a Gaussian pulse’s duration followed by a measurement measure_sched, i.e. a Rabi experiment. You can reuse the block object for every scanned duration by assigning a target duration value.

  • Added a new function array_to_latex() to the qiskit.visualization module that can be used to represent and visualize vectors and matrices with LaTeX.

    from qiskit.visualization import array_to_latex
    from numpy import sqrt, exp, pi
    mat = [[0, exp(pi*.75j)],
           [1/sqrt(8), 0.875]]
    array_to_latex(mat)
  • The Statevector and DensityMatrix classes now have draw() methods which allow objects to be drawn as either text matrices, IPython Latex objects, Latex source, Q-spheres, Bloch spheres and Hinton plots. By default the output type is the equivalent output from __repr__ but this default can be changed in a user config file by setting the state_drawer option. For example:

    from qiskit.quantum_info import DensityMatrix
    dm = DensityMatrix.from_label('r0')
    dm.draw('latex')
    from qiskit.quantum_info import Statevector
    sv = Statevector.from_label('+r')
    sv.draw('qsphere')

    Additionally, the draw() method is now used for the ipython display of these classes, so if you change the default output type in a user config file then when a Statevector or a DensityMatrix object are displayed in a jupyter notebook that output type will be used for the object.

  • Pulse qiskit.pulse.Instruction objects and parametric pulse objects (eg Gaussian now support using Parameter and ParameterExpression objects for the duration parameter. For example:

    from qiskit.circuit import Parameter
    from qiskit.pulse import Gaussian
     
    dur = Parameter('x_pulse_duration')
    double_dur = dur * 2
    rx_pulse = Gaussian(dur, 0.1, dur/4)
    double_rx_pulse = Gaussian(double_dir, 0.1, dur/4)

    Note that while we can create an instruction with a parameterized duration adding an instruction with unbound parameter duration to a schedule is supported only by the newly introduced representation ScheduleBlock. See the known issues release notes section for more details.

  • The run() method for the QasmSimulatorPy, StatevectorSimulatorPy, and UnitarySimulatorPy backends now takes a QuantumCircuit (or a list of QuantumCircuit objects) as its input. The previous QasmQobj object is still supported for now, but will be deprecated in a future release.

    For an example of how to use this see:

    from qiskit import transpile, QuantumCircuit
     
    from qiskit.providers.basicaer import BasicAer
     
    backend = BasicAer.get_backend('qasm_simulator')
     
    circuit = QuantumCircuit(2)
    circuit.h(0)
    circuit.cx(0, 1)
    circuit.measure_all()
     
    tqc = transpile(circuit, backend)
    result = backend.run(tqc, shots=4096).result()
  • The CommutativeCancellation transpiler pass has a new optional kwarg on the constructor basis_gates, which takes the a list of the names of basis gates for the target backend. When specified the pass will only use gates in the basis_gates kwarg. Previously, the pass would automatically replace consecutive gates which commute with ZGate with the U1Gate unconditionally. The basis_gates kwarg enables you to specify which z-rotation gates are present in the target basis to avoid this.

  • The constructors of the Bit class and subclasses, Qubit, Clbit, and AncillaQubit, have been updated such that their two parameters, register and index are now optional. This enables the creation of bit objects that are independent of a register.

  • A new class, BooleanExpression, has been added to the qiskit.circuit.classicalfunction module. This class allows for creating an oracle from a Python boolean expression. For example:

    from qiskit.circuit import BooleanExpression, QuantumCircuit
     
    expression = BooleanExpression('~x & (y | z)')
    circuit = QuantumCircuit(4)
    circuit.append(expression, [0, 1, 2, 3])
    circuit.draw('mpl')
    circuit.decompose().draw('mpl')

    The BooleanExpression also includes a method, from_dimacs_file(), which allows loading formulas described in the DIMACS-CNF(opens in a new tab) format. For example:

    from qiskit.circuit import BooleanExpression, QuantumCircuit
     
    boolean_exp = BooleanExpression.from_dimacs_file("simple_v3_c2.cnf")
    circuit = QuantumCircuit(boolean_exp.num_qubits)
    circuit.append(boolean_exp, range(boolean_exp.num_qubits))
    circuit.draw('text')
         ┌───────────────────┐
    q_0:0
         │                   │
    q_1:1
         │  SIMPLE_V3_C2.CNF │
    q_2:2
         │                   │
    q_3:3
         └───────────────────┘
    circuit.decompose().draw('text')
    q_0: ──o────o────────────
           │    │
    q_1: ──■────o────■───────
           │    │    │
    q_2: ──■────┼────o────■──
         ┌─┴─┐┌─┴─┐┌─┴─┐┌─┴─┐
    q_3: ┤ X ├┤ X ├┤ X ├┤ X ├
         └───┘└───┘└───┘└───┘
  • Added a new class, PhaseOracle, has been added to the qiskit.circuit.library module. This class enables the construction of phase oracle circuits from Python boolean expressions.

    from qiskit.circuit.library.phase_oracle import PhaseOracle
     
    oracle = PhaseOracle('x1 & x2 & (not x3)')
    oracle.draw('mpl')

    These phase oracles can be used as part of a larger algorithm, for example with qiskit.algorithms.AmplificationProblem:

    from qiskit.algorithms import AmplificationProblem, Grover
    from qiskit import BasicAer
     
    backend = BasicAer.get_backend('qasm_simulator')
     
    problem = AmplificationProblem(oracle, is_good_state=oracle.evaluate_bitstring)
    grover = Grover(quantum_instance=backend)
    result = grover.amplify(problem)
    result.top_measurement

    The PhaseOracle class also includes a from_dimacs_file() method which enables constructing a phase oracle from a file describing a formula in the DIMACS-CNF(opens in a new tab) format.

    from qiskit.circuit.library.phase_oracle import PhaseOracle
     
    oracle = PhaseOracle.from_dimacs_file("simple_v3_c2.cnf")
    oracle.draw('text')
    state_0: ─o───────o──────────────
              │ ┌───┐ │ ┌───┐
    state_1: ─■─┤ X ├─■─┤ X ├─■──────
              │ └───┘   └───┘ │ ┌───┐
    state_2: ─■───────────────o─┤ Z ├
                                └───┘
  • All transpiler passes (ie any instances of BasePass) are now directly callable. Calling a pass provides a convenient interface for running the pass on a QuantumCircuit object.

    For example, running a single transformation pass, such as BasisTranslator, can be done with:

    from qiskit import QuantumCircuit
    from qiskit.transpiler.passes import BasisTranslator
    from qiskit.circuit.equivalence_library import SessionEquivalenceLibrary as sel
     
    circuit = QuantumCircuit(1)
    circuit.h(0)
     
    pass_instance = BasisTranslator(sel, ['rx', 'rz', 'cx'])
    result = pass_instance(circuit)
    result.draw(output='mpl')

    When running an analysis pass, a property set (as dict or as PropertySet) needs to be added as a parameter and it might be modified “in-place”. For example:

    from qiskit import QuantumCircuit
    from qiskit.transpiler.passes import Depth
     
    circuit = QuantumCircuit(1)
    circuit.h(0)
     
    property_set = {}
    pass_instance = Depth()
    pass_instance(circuit, property_set)
    print(property_set)
  • The QasmQobjConfig class now has an optional kwarg for meas_level and meas_return. These fields can be used to enable generating QasmQobj job payloads that support meas_level=1 (kerneled data) for circuit jobs (previously this was only exposed for PulseQobj objects). The assemble() function has been updated to set this field for QasmQobj objects it generates.

  • A new tensor() method has been added to the QuantumCircuit class. This method enables tensoring another circuit with an existing circuit. This method works analogously to qiskit.quantum_info.Operator.tensor() and is consistent with the little-endian convention of Qiskit.

    For example:

    from qiskit import QuantumCircuit
    top = QuantumCircuit(1)
    top.x(0);
    bottom = QuantumCircuit(2)
    bottom.cry(0.2, 0, 1);
    bottom.tensor(top).draw(output='mpl')
  • The qiskit.circuit.QuantumCircuit class now supports arbitrary free form metadata with the metadata attribute. A user (or program built on top of QuantumCircuit) can attach metadata to a circuit for use in tracking the circuit. For example:

    from qiskit.circuit import QuantumCircuit
     
    qc = QuantumCircuit(2, user_metadata_field_1='my_metadata',
                        user_metadata_field_2='my_other_value')

    or:

    from qiskit.circuit import QuantumCircuit
     
    qc = QuantumCircuit(2)
    qc.metadata = {'user_metadata_field_1': 'my_metadata',
                   'user_metadata_field_2': 'my_other_value'}

    This metadata will not be used for influencing the execution of the circuit but is just used for tracking the circuit for the lifetime of the object. The metadata attribute will persist between any circuit transforms including transpile() and assemble(). The expectation is for providers to associate the metadata in the result it returns, so that users can filter results based on circuit metadata the same way they can currently do with QuantumCircuit.name.

  • Add a new operator class CNOTDihedral has been added to the qiskit.quantum_info module. This class is used to represent the CNOT-Dihedral group, which is generated by the quantum gates CXGate, TGate, and XGate.

  • Adds a & (__and__) binary operator to BaseOperator subclasses (eg qiskit.quantum_info.Operator) in the qiskit.quantum_info module. This is shorthand to call the classes compose() method (ie A & B == A.compose(B)).

    For example:

    import qiskit.quantum_info as qi
     
    qi.Pauli('X') & qi.Pauli('Y')
  • Adds a & (__and__) binary operator to qiskit.quantum_info.Statevector and qiskit.quantum_info.DensityMatrix classes. This is shorthand to call the classes evolve() method (ie psi & U == psi.evolve(U)).

    For example:

    import qiskit.quantum_info as qi
     
    qi.Statevector.from_label('0') & qi.Pauli('X')
  • A new a new 2-qubit gate, ECRGate, the echo cross-resonance (ECR), has been added to the qiskit.circuit.library module along with a corresponding method, ecr() for the QuantumCircuit class. The ECR gate is two CR(π4)CR(\frac{π}{4}) pulses with an XGate between them for the echo. This gate is locally equivalent to a CXGate (can convert to a CNOT with local pre- or post-rotation). It is the native gate on current IBM hardware and compiling to it allows the pre-/post-rotations to be merged into the rest of the circuit.

  • A new kwarg approximation_degree has been added to the transpile() function for enabling approximate compilation. Valid values range from 0 to 1, and higher means less approximation. This is a heuristic dial to experiment with circuit approximations. The concrete interpretation of this number is left to each pass, which may use it to perform some approximate version of the pass. Specific examples include the UnitarySynthesis pass or the or translators to discrete gate sets. If a pass does not support this option, it implies exact transformation.

  • Two new transpiler passess, GateDirection and qiskit.transpiler.passes.CheckGateDirection, were added to the qiskit.transpiler.passes module. These new passes are inteded to be more general replacements for CXDirection and CheckCXDirection (which are both now deprecated, see the deprecation notes for more details) that perform the same function but work with other gates beside just CXGate.

  • When running on Windows, parallel execution with the parallel_map() function can now be enabled (it is still disabled by default). To do this you can either set parallel = True in a user config file, or set the QISKIT_PARALLEL environment variable to TRUE (this will also effect transpile() and assemble() which both use parallel_map() internally). It is important to note that when enabling parallelism on Windows there are limitations around how Python launches processes for Windows, see the Known Issues section below for more details on the limitations with parallel execution on Windows.

  • A new function, hellinger_distance(), for computing the Hellinger distance between two counts distributions has been added to the qiskit.quantum_info module.

  • The decompose_clifford() function in the qiskit.quantum_info module (which gets used internally by the qiskit.quantum_info.Clifford.to_circuit() method) has a new kwarg method which enables selecting the synthesis method used by either setting it to 'AG' or 'greedy'. By default for more than three qubits it is set to 'greedy' which uses a non-optimal greedy compilation routine for Clifford elements synthesis, by Bravyi et. al., which typically yields better CX cost compared to the previously used Aaronson-Gottesman method (for more than two qubits). You can use the method kwarg to revert to the previous default Aaronson-Gottesman method by setting method='AG'.

  • The Initialize class in the qiskit.extensions module can now be constructed using an integer. The ‘1’ bits of the integer will insert a Reset and an XGate into the circuit for the corresponding qubit. This will be done using the standard little-endian convention is qiskit, ie the rightmost bit of the integer will set qubit 0. For example, setting the parameter in Initialize equal to 5 will set qubits 0 and 2 to value 1.

    from qiskit.extensions import Initialize
     
    initialize = Initialize(13)
    initialize.definition.draw('mpl')
  • The Initialize class in the qiskit.extensions module now supports constructing directly from a Pauli label (analogous to the qiskit.quantum_info.Statevector.from_label() method). The Pauli label refer to basis states of the Pauli eigenstates Z, X, Y. These labels use Qiskit’s standard little-endian notation, for example a label of '01' would initialize qubit 0 to 1|1\rangle and qubit 1 to 0|0\rangle.

    from qiskit.extensions import Initialize
     
    initialize = Initialize("10+-lr")
    initialize.definition.draw('mpl')
  • The kwarg, template_list, for the constructor of the qiskit.transpiler.passes.TemplateOptimization transpiler pass now supports taking in a list of both QuantumCircuit and DAGDependency objects. Previously, only QuantumCircuit were accepted (which were internally converted to DAGDependency objects) in the input list.

  • A new transpiler pass, qiskit.transpiler.passes.RZXCalibrationBuilder, capable of generating calibrations and adding them to a quantum circuit has been introduced. This pass takes calibrated CXGate objects and creates the calibrations for qiskit.circuit.library.RZXGate objects with an arbitrary rotation angle. The schedules are created by stretching and compressing the GaussianSquare pulses of the echoed-cross resonance gates.

  • New template circuits for using qiskit.circuit.library.RZXGate are added to the qiskit.circuit.library module (eg rzx_yz). This enables pairing the TemplateOptimization pass with the qiskit.transpiler.passes.RZXCalibrationBuilder pass to automatically find and replace gate sequences, such as CNOT - P(theta) - CNOT, with more efficent circuits based on qiskit.circuit.library.RZXGate with a calibration.

  • The matplotlib output type for the circuit_drawer() and the draw() method for the QuantumCircuit class now supports configuration files for setting the visualization style. In previous releases, there was basic functionality that allowed users to pass in a style kwarg that took in a dict to customize the colors and other display features of the mpl drawer. This has now been expanded so that these dictionaries can be loaded from JSON files directly without needing to pass a dictionary. This enables users to create new style files and use that style for visualizations by passing the style filename as a string to the style kwarg.

    To leverage this feature you must set the circuit_mpl_style_path option in a user config file. This option should be set to the path you want qiskit to search for style JSON files. If specifying multiple path entries they should be separated by :. For example, setting circuit_mpl_style_path = ~/.qiskit:~/user_styles in a user config file will look for JSON files in both ~/.qiskit and ~/user_styles.

  • A new kwarg, format_marginal has been added to the function marginal_counts() which when set to True formats the counts output according to the cregs in the circuit and missing indices are represented with a _. For example:

    from qiskit import QuantumCircuit, execute, BasicAer, result
    from qiskit.result.utils import marginal_counts
    qc = QuantumCircuit(5, 5)
    qc.x(0)
    qc.measure(0, 0)
     
    result = execute(qc, BasicAer.get_backend('qasm_simulator')).result()
    print(marginal_counts(result.get_counts(), [0, 2, 4], format_marginal=True))
  • Improved the performance of qiskit.quantum_info.Statevector.expectation_value() and qiskit.quantum_info.DensityMatrix.expectation_value() when the argument operator is a Pauli or SparsePauliOp operator.

  • The user config file has 2 new configuration options, num_processes and parallel, which are used to control the default behavior of parallel_map(). The parallel option is a boolean that is used to dictate whether parallel_map() will run in multiple processes or not. If it set to False calls to parallel_map() will be executed serially, while setting it to True will enable parallel execution. The num_processes option takes an integer which sets how many CPUs to use when executing in parallel. By default it will use the number of CPU cores on a system.

  • There are 2 new environment variables, QISKIT_PARALLEL and QISKIT_NUM_PROCS, that can be used to control the default behavior of parallel_map(). The QISKIT_PARALLEL option can be set to the TRUE (any capitalization) to set the default to run in multiple processes when parallel_map() is called. If it is set to any other value parallel_map() will be executed serially. QISKIT_NUM_PROCS takes an integer (for example QISKIT_NUM_PROCS=5) which will be used as the default number of processes to run with. Both of these will take precedence over the equivalent option set in the user config file.

  • A new method, gradient(), has been added to the ParameterExpression class. This method is used to evaluate the gradient of a ParameterExpression object.

  • The __eq__ method (ie what is called when the == operator is used) for the ParameterExpression now allows for the comparison with a numeric value. Previously, it was only possible to compare two instances of ParameterExpression with ==. For example:

    from qiskit.circuit import Parameter
     
    x = Parameter("x")
    y = x + 2
    y = y.assign(x, -1)
     
    assert y == 1
  • The PauliFeatureMap class in the qiskit.circuit.library module now supports adjusting the rotational factor, α\alpha, by either setting using the kwarg alpha on the constructor or setting the alpha attribute after creation. Previously this value was fixed at 2.0. Adjusting this attribute allows for better control of decision boundaries and provides additional flexibility handling the input features without needing to explicitly scale them in the data set.

  • A new Gate class, PauliGate, has been added the qiskit.circuit.library module and corresponding method, pauli(), was added to the QuantumCircuit class. This new gate class enables applying several individual pauli gates to different qubits at the simultaneously. This is primarily useful for simulators which can use this new gate to more efficiently implement multiple simultaneous Pauli gates.

  • Improve the qiskit.quantum_info.Pauli operator. This class now represents and element from the full N-qubit Pauli group including complex coefficients. It now supports the Operator API methods including compose(), dot(), tensor() etc, where compose and dot are defined with respect to the full Pauli group.

    This class also allows conversion to and from the string representation of Pauli’s for convenience.

    For example

    from qiskit.quantum_info import Pauli
     
    P1 = Pauli('XYZ')
    P2 = Pauli('YZX')
    P1.dot(P2)

    Pauli’s can also be directly appended to QuantumCircuit objects

    from qiskit import QuantumCircuit
    from qiskit.quantum_info import Pauli
     
    circ = QuantumCircuit(3)
    circ.append(Pauli('XYZ'), [0, 1, 2])
    circ.draw(output='mpl')

    Additional methods allow computing when two Pauli’s commute (using the commutes() method) or anticommute (using the anticommutes() method), and computing the Pauli resulting from Clifford conjugation P=C.P.CP^\prime = C.P.C^\dagger using the evolve() method.

    See the API documentation of the Pauli class for additional information.

  • A new function, random_pauli(), for generating a random element of the N-qubit Pauli group has been added to the qiskit.quantum_info module.

  • A new class, PiecewisePolynomialPauliRotations, has been added to the qiskit.circuit.library module. This circuit library element is used for mapping a piecewise polynomial function, f(x)f(x), which is defined through breakpoints and coefficients, on qubit amplitudes. The breakpoints (x0,...,xJ)(x_0, ..., x_J) are a subset of [0,2n1][0, 2^n-1], where nn is the number of state qubits. The corresponding coefficients [aj,1,...,aj,d][a_{j,1},...,a_{j,d}], where dd is the highest degree among all polynomials. Then f(x)f(x) is defined as:

    f(x)={0,x<x0i=0i=daj,ixi,xjx<xj+1\begin{split}f(x) = \begin{cases} 0, x < x_0 \\ \sum_{i=0}^{i=d}a_{j,i} x^i, x_j \leq x < x_{j+1} \end{cases}\end{split}

    where we implicitly assume xJ+1=2nx_{J+1} = 2^n. And the mapping applied to the amplitudes is given by

    Fx0=cos(pj(x))x0+sin(pj(x))x1F|x\rangle |0\rangle = \cos(p_j(x))|x\rangle |0\rangle + \sin(p_j(x))|x\rangle |1\rangle

    This mapping is based on controlled Pauli Y-rotations and constructed using the PolynomialPauliRotations.

  • A new module qiskit.algorithms has been introduced. This module contains functionality equivalent to what has previously been provided by the qiskit.aqua.algorithms module (which is now deprecated) and provides the building blocks for constructing quantum algorithms. For details on migrating from qiskit-aqua to this new module, please refer to the migration guide(opens in a new tab).

  • A new module qiskit.opflow has been introduced. This module contains functionality equivalent to what has previously been provided by the qiskit.aqua.operators module (which is now deprecated) and provides the operators and state functions which are used to build quantum algorithms. For details on migrating from qiskit-aqua to this new module, please refer to the migration guide(opens in a new tab).

  • This is the first release that includes precompiled binary wheels for the for Linux aarch64 systems. If you are running a manylinux2014 compatible aarch64 Linux system there are now precompiled wheels available on PyPI, you are no longer required to build from source to install qiskit-terra.

  • The qiskit.quantum_info.process_fidelity() function is now able to be used with a non-unitary target channel. In this case the returned value is equivalent to the qiskit.quantum_info.state_fidelity() of the normalized qiskit.quantum_info.Choi matrices for the channels.

    Note that the qiskit.quantum_info.average_gate_fidelity() and qiskit.quantum_info.gate_error() functions still require the target channel to be unitary and will raise an exception if it is not.

  • Added a new pulse builder function, qiskit.pulse.macro(). This enables normal Python functions to be decorated as macros. This enables pulse builder functions to be used within the decorated function. The builder macro can then be called from within a pulse building context, enabling code reuse.

    For Example:

    from qiskit import pulse
     
    @pulse.macro
    def measure(qubit: int):
        pulse.play(pulse.GaussianSquare(16384, 256, 15872),
                   pulse.MeasureChannel(qubit))
        mem_slot = pulse.MemorySlot(0)
        pulse.acquire(16384, pulse.AcquireChannel(0), mem_slot)
        return mem_slot
     
    with pulse.build(backend=backend) as sched:
        mem_slot = measure(0)
        print(f"Qubit measured into {mem_slot}")
     
    sched.draw()
  • A new class, PauliTwoDesign, was added to the qiskit.circuit.library which implements a particular form of a 2-design circuit from https://arxiv.org/pdf/1803.11173.pdf(opens in a new tab) For instance, this circuit can look like:

    from qiskit.circuit.library import PauliTwoDesign
    circuit = PauliTwoDesign(4, reps=2, seed=5, insert_barriers=True)
    circuit.decompose().draw(output='mpl')
  • A new pulse drawer qiskit.visualization.pulse_v2.draw() (which is aliased as qiskit.visualization.pulse_drawer_v2) is now available. This new pulse drawer supports multiple new features not present in the original pulse drawer (pulse_drawer()).

    • Truncation of long pulse instructions.
    • Visualization of parametric pulses.
    • New stylesheets IQXStandard, IQXSimple, IQXDebugging.
    • Visualization of system info (channel frequency, etc…) by specifying qiskit.providers.Backend objects for visualization.
    • Specifying axis objects for plotting to allow further extension of generated plots, i.e., for publication manipulations.

    New stylesheets can take callback functions that dynamically modify the apperance of the output image, for example, reassembling a collection of channels, showing details of instructions, updating appearance of pulse envelopes, etc… You can create custom callback functions and feed them into a stylesheet instance to modify the figure appearance without modifying the drawer code. See pulse drawer module docstrings for details.

    Note that file saving is now delegated to Matplotlib. To save image files, you need to call savefig method with returned Figure object.

  • Adds a reverse_qargs() method to the qiskit.quantum_info.Statevector and qiskit.quantum_info.DensityMatrix classes. This method reverses the order of subsystems in the states and is equivalent to the qiskit.circuit.QuantumCircuit.reverse_bits() method for N-qubit states. For example:

    from qiskit.circuit.library import QFT
    from qiskit.quantum_info import Statevector
     
    circ = QFT(3)
     
    state1 = Statevector.from_instruction(circ)
    state2 = Statevector.from_instruction(circ.reverse_bits())
     
    state1.reverse_qargs() == state2
  • Adds a reverse_qargs() method to the qiskit.quantum_info.Operator class. This method reverses the order of subsystems in the operator and is equivalent to the qiskit.circuit.QuantumCircuit.reverse_bits() method for N-qubit operators. For example:

    from qiskit.circuit.library import QFT
    from qiskit.quantum_info import Operator
     
    circ = QFT(3)
     
    op1 = Operator(circ)
    op2 = Operator(circ.reverse_bits())
     
    op1.reverse_qargs() == op2
  • The latex output method for the qiskit.visualization.circuit_drawer() function and the draw() method now will use a user defined label on gates in the output visualization. For example:

    import math
     
    from qiskit.circuit import QuantumCircuit
     
    qc = QuantumCircuit(2)
    qc.h(0)
    qc.rx(math.pi/2, 0, label='My Special Rotation')
     
    qc.draw(output='latex')
  • The routing_method kwarg for the transpile() function now accepts a new option, 'none'. When routing_method='none' no routing pass will be run as part of the transpilation. If the circuit does not fit coupling map a TranspilerError exception will be raised.

  • A new gate class, RVGate, was added to the qiskit.circuit.library module along with the corresponding QuantumCircuit method rv(). The RVGate is a general rotation gate, similar to the UGate, but instead of specifying Euler angles the three components of a rotation vector are specified where the direction of the vector specifies the rotation axis and the magnitude specifies the rotation angle about the axis in radians. For example:

    import math
     
    import np
     
    from qiskit.circuit import QuantumCircuit
     
    qc = QuantumCircuit(1)
    theta = math.pi / 5
    phi = math.pi / 3
    # RGate axis:
    axis = np.array([math.cos(phi), math.sin(phi)])
    rotation_vector = theta * axis
    qc.rv(*rotation_vector, 0)
  • Unbound Parameter objects used in a QuantumCircuit object will now be sorted by name. This will take effect for the parameters returned by the parameters attribute. Additionally, the qiskit.circuit.QuantumCircuit.bind_parameters() and qiskit.circuit.QuantumCircuit.assign_parameters() methods can now take in a list of a values which will bind/assign them to the parameters in name-sorted order. Previously these methods would only take a dictionary of parameters and values. For example:

    from qiskit.circuit import QuantumCircuit, Parameter
     
    circuit = QuantumCircuit(1)
    circuit.rx(Parameter('x'), 0)
    circuit.ry(Parameter('y'), 0)
     
    print(circuit.parameters)
     
    bound = circuit.bind_parameters([1, 2])
    bound.draw(output='mpl')
  • The constructors for the qiskit.quantum_info.Statevector and qiskit.quantum_info.DensityMatrix classes can now take a QuantumCircuit object in to build a Statevector and DensityMatrix object from that circuit, assuming that the qubits are initialized in 0|0\rangle. For example:

    from qiskit import QuantumCircuit
    from qiskit.quantum_info import Statevector
     
    qc = QuantumCircuit(2)
    qc.h(0)
    qc.cx(0, 1)
     
    statevector = Statevector(qc)
    statevector.draw(output='latex')
  • New fake backend classes are available under qiskit.test.mock. These included mocked versions of ibmq_casablanca, ibmq_sydney, ibmq_mumbai, ibmq_lima, ibmq_belem, ibmq_quito. As with the other fake backends, these include snapshots of calibration data (i.e. backend.defaults()) and error data (i.e. backend.properties()) taken from the real system, and can be used for local testing, compilation and simulation.

Known Issues

  • Attempting to add an qiskit.pulse.Instruction object with a parameterized duration (ie the value of duration is an unbound Parameter or ParameterExpression object) to a qiskit.pulse.Schedule is not supported. Attempting to do so will result in UnassignedDurationError PulseError being raised. This is a limitation of how the Instruction overlap constraints are evaluated currently. This is supported by ScheduleBlock, in which the overlap constraints are evaluated just before the execution.

  • On Windows systems when parallel execution is enabled for parallel_map() parallelism may not work when called from a script running outside of a if __name__ == '__main__': block. This is due to how Python launches parallel processes on Windows. If a RuntimeError or AttributeError are raised by scripts that call parallel_map() (including using functions that use parallel_map() internally like transpile()) with Windows and parallelism enabled you can try embedding the script calls inside if __name__ == '__main__': to workaround the issue. For example:

    from qiskit import QuantumCircuit, QiskitError
    from qiskit import execute, Aer
     
    qc1 = QuantumCircuit(2, 2)
    qc1.h(0)
    qc1.cx(0, 1)
    qc1.measure([0,1], [0,1])
    # making another circuit: superpositions
    qc2 = QuantumCircuit(2, 2)
    qc2.h([0,1])
    qc2.measure([0,1], [0,1])
    execute([qc1, qc2], Aer.get_backend('qasm_simulator'))

    should be changed to:

    from qiskit import QuantumCircuit, QiskitError
    from qiskit import execute, Aer
     
    def main():
        qc1 = QuantumCircuit(2, 2)
        qc1.h(0)
        qc1.cx(0, 1)
        qc1.measure([0,1], [0,1])
        # making another circuit: superpositions
        qc2 = QuantumCircuit(2, 2)
        qc2.h([0,1])
        qc2.measure([0,1], [0,1])
        execute([qc1, qc2], Aer.get_backend('qasm_simulator'))
     
    if __name__ == '__main__':
        main()

    if any errors are encountered with parallelism on Windows.

Upgrade Notes

  • The preset pass managers level_1_pass_manager, level_2_pass_manager, and level_3_pass_manager (which are used for optimization_level 1, 2, and 3 in the transpile() and execute() functions) now unconditionally use the Optimize1qGatesDecomposition pass for 1 qubit gate optimization. Previously, these pass managers would use the Optimize1qGates pass if the basis gates contained u1, u2, or u3. If you want to still use the old Optimize1qGates you will need to construct a custom PassManager with the pass.

  • Following transpilation of a parameterized QuantumCircuit, the global_phase attribute of output circuit may no longer be returned in a simplified form, if the global phase is a ParameterExpression.

    For example:

    qc = QuantumCircuit(1)
    theta = Parameter('theta')
     
    qc.rz(theta, 0)
    qc.rz(-theta, 0)
     
    print(transpile(qc, basis_gates=['p']).global_phase)

    previously returned 0, but will now return -0.5*theta + 0.5*theta. This change was necessary was to avoid a large runtime performance penalty as simplifying symbolic expressions can be quite slow, especially if there are many ParameterExpression objects in a circuit.

  • The BasicAerJob job objects returned from BasicAer backends are now synchronous instances of JobV1. This means that calls to the run() will block until the simulation finishes executing. If you want to restore the previous async behavior you’ll need to wrap the run() with something that will run in a seperate thread or process like futures.ThreadPoolExecutor or futures.ProcessPoolExecutor.

  • The allow_sample_measuring option for the BasicAer simulator QasmSimulatorPy has changed from a default of False to True. This was done to better reflect the actual default behavior of the simulator, which would use sample measuring if the input circuit supported it (even if it was not enabled). If you are running a circuit that doesn’t support sample measurement (ie it has Reset operations or if there are operations after a measurement on a qubit) you should make sure to explicitly set this option to False when you call run().

  • The CommutativeCancellation transpiler pass is now aware of the target basis gates, which means it will only use gates in the specified basis. Previously, the pass would unconditionally replace consecutive gates which commute with ZGate with the U1Gate. However, now that the pass is basis aware and has a kwarg, basis_gates, for specifying the target basis there is a potential change in behavior if the kwarg is not set. When the basis_gates kwarg is not used and there are no variable z-rotation gates in the circuit then no commutative cancellation will occur.

  • Register (which is the parent class for QuantumRegister and ClassicalRegister and Bit (which is the parent class for Qubit and Clbit) objects are now immutable. In previous releases it was possible to adjust the value of a size or name attributes of a Register object and the index or register attributes of a Bit object after it was initially created. However this would lead to unsound behavior that would corrupt container structure that rely on a hash (such as a dict) since these attributes are treated as immutable properties of a register or bit (see #4705(opens in a new tab) for more details). To avoid this unsound behavior this attributes of a Register and Bit are no longer settable after initial creation. If you were previously adjusting the objects at runtime you will now need to create a new Register or Bit object with the new values.

  • The DAGCircuit.__eq__ method (which is used by the == operator), which is used to check structural equality of DAGCircuit and QuantumCircuit instances, will now include the global_phase and calibrations attributes in the fields checked for equality. This means that circuits which would have evaluated as equal in prior releases may not anymore if the global_phase or calibrations differ between the circuits. For example, in previous releases this would return True:

    import math
     
    from qiskit import QuantumCircuit
     
    qc1 = QuantumCircuit(1)
    qc1.x(0)
     
    qc2 = QuantumCircuit(1, global_phase=math.pi)
    qc2.x(0)
     
    print(qc2 == qc1)

    However, now because the global_phase attribute of the circuits differ this will now return False.

  • The previously deprecated qubits() and clbits() methods on the DAGCircuit class, which were deprecated in the 0.15.0 Terra release, have been removed. Instead you should use the qubits and clbits attributes of the DAGCircuit class. For example, if you were running:

    from qiskit.dagcircuit import DAGCircuit
     
    dag = DAGCircuit()
    qubits = dag.qubits()

    That would be replaced by:

    from qiskit.dagcircuit import DAGCircuit
     
    dag = DAGCircuit()
    qubits = dag.qubits
  • The PulseDefaults returned by the fake pulse backends qiskit.test.mock.FakeOpenPulse2Q and qiskit.test.mock.FakeOpenPulse3Q have been updated to have more realistic pulse sequence definitions. If you are using these fake backend classes you may need to update your usage because of these changes.

  • The default synthesis method used by decompose_clifford() function in the quantum_info module (which gets used internally by the qiskit.quantum_info.Clifford.to_circuit() method) for more than 3 qubits now uses a non-optimal greedy compilation routine for Clifford elements synthesis, by Bravyi et. al., which typically yields better CX cost compared to the old default. If you need to revert to the previous Aaronson-Gottesman method this can be done by setting method='AG'.

  • The previously deprecated module qiskit.visualization.interactive, which was deprecated in the 0.15.0 release, has now been removed. Instead you should use the matplotlib based visualizations:

    Removed Interactive functionEquivalent matplotlib function
    iplot_bloch_multivectorqiskit.visualization.plot_bloch_multivector()
    iplot_state_cityqiskit.visualization.plot_state_city()
    iplot_state_qsphereqiskit.visualization.plot_state_qsphere()
    iplot_state_hintonqiskit.visualization.plot_state_hinton()
    iplot_histogramqiskit.visualization.plot_histogram()
    iplot_state_paulivecqiskit.visualization.plot_state_paulivec()
  • The qiskit.Aer and qiskit.IBMQ top level attributes are now lazy loaded. This means that the objects will now always exist and warnings will no longer be raised on import if qiskit-aer or qiskit-ibmq-provider are not installed (or can’t be found by Python). If you were checking for the presence of qiskit-aer or qiskit-ibmq-provider using these module attributes and explicitly comparing to None or looking for the absence of the attribute this no longer will work because they are always defined as an object now. In other words running something like:

    try:
        from qiskit import Aer
    except ImportError:
        print("Aer not available")
     
    or::
     
    try:
        from qiskit import IBMQ
    except ImportError:
        print("IBMQ not available")

    will no longer work. Instead to determine if those providers are present you can either explicitly use qiskit.providers.aer.Aer and qiskit.providers.ibmq.IBMQ:

    try:
        from qiskit.providers.aer import Aer
    except ImportError:
        print("Aer not available")
     
    try:
        from qiskit.providers.ibmq import IBMQ
    except ImportError:
        print("IBMQ not available")

    or check bool(qiskit.Aer) and bool(qiskit.IBMQ) instead, for example:

    import qiskit
     
    if not qiskit.Aer:
        print("Aer not available")
    if not qiskit.IBMQ:
        print("IBMQ not available")

    This change was necessary to avoid potential import cycle issues between the qiskit packages and also to improve the import time when Aer or IBMQ are not being used.

  • The user config file option suppress_packaging_warnings option in the user config file and the QISKIT_SUPPRESS_PACKAGING_WARNINGS environment variable no longer has any effect and will be silently ignored. The warnings this option controlled have been removed and will no longer be emitted at import time from the qiskit module.

  • The previously deprecated condition kwarg for qiskit.dagcircuit.DAGNode constructor has been removed. It was deprecated in the 0.15.0 release. Instead you should now be setting the classical condition on the Instruction object passed into the DAGNode constructor when creating a new op node.

  • When creating a new Register (which is the parent class for QuantumRegister and ClassicalRegister) or QuantumCircuit object with a number of bits (eg QuantumCircuit(2)), it is now required that number of bits are specified as an integer or another type which is castable to unambiguous integers(e.g. 2.0). Non-integer values will now raise an error as the intent in those cases was unclear (you can’t have fractional bits). For more information on why this was changed refer to: #4855(opens in a new tab)

  • networkx(opens in a new tab) is no longer a requirement for qiskit-terra. All the networkx usage inside qiskit-terra has been removed with the exception of 3 methods:

    • qiskit.dagcircuit.DAGCircuit.to_networkx
    • qiskit.dagcircuit.DAGCircuit.from_networkx
    • qiskit.dagcircuit.DAGDependency.to_networkx

    If you are using any of these methods you will need to manually install networkx in your environment to continue using them.

  • By default on macOS with Python >=3.8 parallel_map() will no longer run in multiple processes. This is a change from previous releases where the default behavior was that parallel_map() would launch multiple processes. This change was made because with newer versions of macOS with Python 3.8 and 3.9 multiprocessing is either unreliable or adds significant overhead because of the change in Python 3.8 to launch new processes with spawn instead of fork. To re-enable parallel execution on macOS with Python >= 3.8 you can use the user config file parallel option or set the environment variable QISKIT_PARALLEL to True.

  • The previously deprecated kwarg callback on the constructor for the PassManager class has been removed. This kwarg has been deprecated since the 0.13.0 release (April, 9th 2020). Instead you can pass the callback kwarg to the qiskit.transpiler.PassManager.run() method directly. For example, if you were using:

    from qiskit.circuit.random import random_circuit
    from qiskit.transpiler import PassManager
     
    qc = random_circuit(2, 2)
     
    def callback(**kwargs)
      print(kwargs['pass_'])
     
    pm = PassManager(callback=callback)
    pm.run(qc)

    this can be replaced with:

    from qiskit.circuit.random import random_circuit
    from qiskit.transpiler import PassManager
     
    qc = random_circuit(2, 2)
     
    def callback(**kwargs)
      print(kwargs['pass_'])
     
    pm = PassManager()
    pm.run(qc, callback=callback)
  • It is now no longer possible to instantiate a base channel without a prefix, such as qiskit.pulse.Channel or qiskit.pulse.PulseChannel. These classes are designed to classify types of different user facing channel classes, such as qiskit.pulse.DriveChannel, but do not have a definition as a target resource. If you were previously directly instantiating either qiskit.pulse.Channel or qiskit.pulse.PulseChannel, this is no longer allowed. Please use the appropriate subclass.

  • When the require_cp and/or require_tp kwargs of qiskit.quantum_info.process_fidelity(), qiskit.quantum_info.average_gate_fidelity(), qiskit.quantum_info.gate_error() are True, they will now only log a warning rather than the previous behavior of raising a QiskitError exception if the input channel is non-CP or non-TP respectively.

  • The QFT class in the qiskit.circuit.library module now computes the Fourier transform using a little-endian representation of tensors, i.e. the state 1|1\rangle maps to 01+2..|0\rangle - |1\rangle + |2\rangle - .. assuming the computational basis correspond to little-endian bit ordering of the integers. 0=000,1=001|0\rangle = |000\rangle, |1\rangle = |001\rangle, etc. This was done to make it more consistent with the rest of Qiskit, which uses a little-endian convention for bit order. If you were depending on the previous bit order you can use the reverse_bits() method to revert to the previous behavior. For example:

    from qiskit.circuit.library import QFT
     
    qft = QFT(5).reverse_bits()
  • The qiskit.__qiskit_version__ module attribute was previously a dict will now return a custom read-only Mapping object that checks the version of qiskit elements at runtime instead of at import time. This was done to speed up the import path of qiskit and eliminate a possible import cycle by only importing the element packages at runtime if the version is needed from the package. This should be fully compatible with the dict previously return and for most normal use cases there will be no difference. However, if some applications were relying on either mutating the contents or explicitly type checking it may require updates to adapt to this change.

  • The qiskit.execute module has been renamed to qiskit.execute_function. This was necessary to avoid a potentical name conflict between the execute() function which is re-exported as qiskit.execute. qiskit.execute the function in some situations could conflict with qiskit.execute the module which would lead to a cryptic error because Python was treating qiskit.execute as the module when the intent was to the function or vice versa. The module rename was necessary to avoid this conflict. If you’re importing qiskit.execute to get the module (typical usage was from qiskit.execute import execute) you will need to update this to use qiskit.execute_function instead. qiskit.execute will now always resolve to the function.

  • The qiskit.compiler.transpile, qiskit.compiler.assemble, qiskit.compiler.schedule, and qiskit.compiler.sequence modules have been renamed to qiskit.compiler.transpiler, qiskit.compiler.assembler, qiskit.compiler.scheduler, and qiskit.compiler.sequence respectively. This was necessary to avoid a potentical name conflict between the modules and the re-exported function paths qiskit.compiler.transpile(), qiskit.compiler.assemble(), qiskit.compiler.schedule(), and qiskit.compiler.sequence(). In some situations this name conflict between the module path and re-exported function path would lead to a cryptic error because Python was treating an import as the module when the intent was to use the function or vice versa. The module rename was necessary to avoid this conflict. If you were using the imports to get the modules before (typical usage would be like``from qiskit.compiler.transpile import transpile``) you will need to update this to use the new module paths. qiskit.compiler.transpile(), qiskit.compiler.assemble(), qiskit.compiler.schedule(), and qiskit.compiler.sequence() will now always resolve to the functions.

  • The qiskit.quantum_info.Quaternion class was moved from the qiskit.quantum_info.operator submodule to the qiskit.quantum_info.synthesis submodule to better reflect it’s purpose. No change is required if you were importing it from the root qiskit.quantum_info module, but if you were importing from qiskit.quantum_info.operator you will need to update your import path.

  • Removed the QuantumCircuit.mcmt method, which has been deprecated since the Qiskit Terra 0.14.0 release in April 2020. Instead of using the method, please use the MCMT class instead to construct a multi-control multi-target gate and use the qiskit.circuit.QuantumCircuit.append() or qiskit.circuit.QuantumCircuit.compose() to add it to a circuit.

    For example, you can replace:

    circuit.mcmt(ZGate(), [0, 1, 2], [3, 4])

    with:

    from qiskit.circuit.library import MCMT
    mcmt = MCMT(ZGate(), 3, 2)
    circuit.compose(mcmt, range(5))
  • Removed the QuantumCircuit.diag_gate method which has been deprecated since the Qiskit Terra 0.14.0 release in April 2020. Instead, use the diagonal() method of QuantumCircuit.

  • Removed the QuantumCircuit.ucy method which has been deprecated since the Qiskit Terra 0.14.0 release in April 2020. Instead, use the ucry() method of QuantumCircuit.

  • The previously deprecated mirror() method for qiskit.circuit.QuantumCircuit has been removed. It was deprecated in the 0.15.0 release. The qiskit.circuit.QuantumCircuit.reverse_ops() method should be used instead since mirroring could be confused with swapping the output qubits of the circuit. The reverse_ops() method only reverses the order of gates that are applied instead of mirroring.

  • The previously deprecated support passing a float (for the scale kwarg as the first positional argument to the qiskit.circuit.QuantumCircuit.draw() has been removed. It was deprecated in the 0.12.0 release. The first positional argument to the qiskit.circuit.QuantumCircuit.draw() method is now the output kwarg which does not accept a float. Instead you should be using scale as a named kwarg instead of using it positionally.

    For example, if you were previously calling draw with:

    from qiskit import QuantumCircuit
     
    qc = QuantumCircuit(2)
    qc.draw(0.75, output='mpl')

    this would now need to be:

    from qiskit import QuantumCircuit
     
    qc = QuantumCircuit(2)
    qc.draw(output='mpl', scale=0.75)

    or:

    qc.draw('mpl', scale=0.75)
  • Features of Qiskit Pulse (qiskit.pulse) which were deprecated in the 0.15.0 release (August, 2020) have been removed. The full set of changes are:

    ModuleOldNew
    qiskit.pulse.librarySamplePulseWaveform
    qiskit.pulse.libraryConstantPulseConstant
    (module rename)pulse.pulse_lib Moduleqiskit.pulse.library
    ClassOld methodNew method
    ParametricPulseget_sample_pulseget_waveform
    InstructioncommandN/A. Commands and Instructions have been unified. Use operands() to get information about the instruction data.
    Acquireacquires, mem_slots, reg_slotsacquire(), mem_slot(), reg_slot(). (The Acquire instruction no longer broadcasts across multiple qubits.)
  • The dictionary previously held on DAGCircuit edges has been removed. Instead, edges now hold the Bit instance which had previously been included in the dictionary as its 'wire' field. Note that the NetworkX graph returned by to_networkx() will still have a dictionary for its edge attributes, but the 'name' field will no longer be populated.

  • The parameters attribute of the QuantumCircuit class no longer is returning a set. Instead it returns a ParameterView object which implements all the methods that set offers (albeit deprecated). This was done to support a model that preserves name-sorted parameters. It should be fully compatible with any previous usage of the set returned by the parameters attribute, except for where explicit type checking of a set was done.

  • When running transpile() on a QuantumCircuit with delay() instructions, the units will be converted to dt if the value of dt (sample time) is known to transpile(), either explicitly via the dt kwarg or via the BackendConfiguration for a Backend object passed in via the backend kwarg.

  • The interpretation of meas_map (which is an attribute of a PulseBackendConfiguration object or as the corresponding meas_map kwarg on the schedule(), assemble(), sequence(), or execute() functions) has been updated to better match the true constraints of the hardware. The format of this data is a list of lists, where the items in the inner list are integers specifying qubit labels. For instance:

    [[A, B, C], [D, E, F, G]]

    Previously, the meas_map constraint was interpreted such that if one qubit was acquired (e.g. A), then all other qubits sharing a subgroup with that qubit (B and C) would have to be acquired at the same time and for the same duration. This constraint has been relaxed. One acquisition does not require more acquisitions. (If A is acquired, B and C do not need to be acquired.) Instead, qubits in the same measurement group cannot be acquired in a partially overlapping way – think of the meas_map as specifying a shared acquisition resource (If we acquire A from t=1000 to t=2000, we cannot acquire B starting from 1000<t<2000). For example:

    # Good
    meas_map = [[0, 1]]
    # Acquire a subset of [0, 1]
    sched = pulse.Schedule()
    sched = sched.append(pulse.Acquire(10, acq_q0))
     
    # Acquire 0 and 1 together (same start time, same duration)
    sched = pulse.Schedule()
    sched = sched.append(pulse.Acquire(10, acq_q0))
    sched = sched.append(pulse.Acquire(10, acq_q1))
     
    # Acquire 0 and 1 disjointly
    sched = pulse.Schedule()
    sched = sched.append(pulse.Acquire(10, acq_q0))
    sched = sched.append(pulse.Acquire(10, acq_q1)) << 10
     
    # Acquisitions overlap, but 0 and 1 aren't in the same measurement
    # grouping
    meas_map = [[0], [1]]
    sched = pulse.Schedule()
    sched = sched.append(pulse.Acquire(10, acq_q0))
    sched = sched.append(pulse.Acquire(10, acq_q1)) << 1
     
    # Bad: 0 and 1 are in the same grouping, but acquisitions
    # partially overlap
    meas_map = [[0, 1]]
    sched = pulse.Schedule()
    sched = sched.append(pulse.Acquire(10, acq_q0))
    sched = sched.append(pulse.Acquire(10, acq_q1)) << 1

Deprecation Notes

  • Two new arguments have been added to qiskit.dagcircuit.DAGNode.semantic_eq(), bit_indices1 and bit_indices2, which are expected to map the Bit instances in each DAGNode to their index in qubits or clbits list of their respective DAGCircuit. During the deprecation period, these arguments are optional and when not specified the mappings will be automatically constructed based on the register and index properties of each Bit instance. However, in a future release, they will be required arguments and the mapping will need to be supplied by the user.

  • The pulse builder functions:

    • qiskit.pulse.call_circuit()
    • qiskit.pulse.call_schedule()

    are deprecated and will be removed in a future release. These functions are unified into qiskit.pulse.call() which should be used instead.

  • The qiskit.pulse.Schedule method qiskit.pulse.Schedule.flatten() method is deprecated and will be removed in a future release. Instead you can use the qiskit.pulse.transforms.flatten() function which will perform the same operation.

  • The assign_parameters() for the following classes:

    and all their subclasses is now deprecated and will be removed in a future release. This functionality has been subsumed ScheduleBlock which is the future direction for constructing parameterized pulse programs.

  • The parameters attribute for the following clasess:

    is deprecated and will be removed in a future release. This functionality has been subsumed ScheduleBlock which is the future direction for constructing parameterized pulse programs.

  • Python 3.6 support has been deprecated and will be removed in a future release. When support is removed you will need to upgrade the Python version you’re using to Python 3.7 or above.

  • Two QuantumCircuit methods combine() and extend() along with their corresponding Python operators + and += are deprecated and will be removed in a future release. Instead the QuantumCircuit method compose() should be used. The compose() method allows more flexibility in composing two circuits that do not have matching registers. It does not, however, automatically add qubits/clbits unlike the deprecated methods. To add a circuit on new qubits/clbits, the qiskit.circuit.QuantumCircuit.tensor() method can be used. For example:

    from qiskit.circuit import QuantumRegister, QuantumCircuit
     
    a = QuantumRegister(2, 'a')
    circuit_a = QuantumCircuit(a)
    circuit_a.cx(0, 1)
     
    b = QuantumRegister(2, 'b')
    circuit_b = QuantumCircuit(b)
    circuit_b.cz(0, 1)
     
    # same as circuit_a + circuit_b (or combine)
    added_with_different_regs = circuit_b.tensor(circuit_a)
     
    # same as circuit_a + circuit_a (or combine)
    added_with_same_regs = circuit_a.compose(circuit_a)
     
    # same as circuit_a += circuit_b (or extend)
    circuit_a = circuit_b.tensor(circuit_a)
     
    # same as circuit_a += circuit_a (or extend)
    circuit_a.compose(circuit_a, inplace=True)
  • Support for passing Qubit instances to the qubits kwarg of the qiskit.transpiler.InstructionDurations.get() method has been deprecated and will be removed in a future release. Instead, you should call the get() method with the integer indices of the desired qubits.

  • Using @ (__matmul__) for invoking the compose method of BaseOperator subclasses (eg Operator) is deprecated and will be removed in a future release. The qiskit.quantum_info.Operator.compose() method can be used directly or also invoked using the & (__and__) operator.

  • Using * (__mul__) for calling the dot() method of BaseOperator subclasses (eg qiskit.quantum_info.Operator) is deprecated and will be removed in a future release. Instead you can just call the dot() directly.

  • Using @ (__matmul__) for invoking the evolve() method of the qiskit.quantum_info.Statevector and qiskit.quantum_info.DensityMatrix classes is deprecated and will be removed in a future release.. The evolve method can be used directly or also invoked using the & (__and__) operator.

  • The qiskit.pulse.schedule.ParameterizedSchedule class has been deprecated and will be removed in a future release. Instead you can directly parameterize pulse Schedule objects with a Parameter object, for example:

    from qiskit.circuit import Parameter
    from qiskit.pulse import Schedule
    from qiskit.pulse import ShiftPhase, DriveChannel
     
    theta = Parameter('theta')
    target_schedule = Schedule()
    target_schedule.insert(0, ShiftPhase(theta, DriveChannel(0)), inplace=True)
  • The qiskit.pulse.ScheduleComponent class in the qiskit.pulse module has been deprecated and will be removed in a future release. Its usage should be replaced either using a qiskit.pulse.Schedule or qiskit.pulse.Instruction directly. Additionally, the primary purpose of the ScheduleComponent class was as a common base class for both Schedule and Instruction for any place that was explicitly type checking or documenting accepting a ScheduleComponent input should be updated to accept Instruction or Schedule.

  • The JSON Schema files and usage for the IBMQ API payloads are deprecated and will be removed in a future release. This includes everything under the qiskit.schemas module and the qiskit.validation module. This also includes the validate kwargs for qiskit.qobj.QasmQobj.to_dict() and qiskit.qobj.QasmQobj.to_dict() along with the module level fastjsonschema validators in qiskit.qobj (which do not raise a deprecation warning). The schema files have been moved to the Qiskit/ibmq-schemas(opens in a new tab) repository and those should be treated as the canonical versions of the API schemas. Moving forward only those schemas will recieve updates and will be used as the source of truth for the schemas. If you were relying on the schemas bundled in qiskit-terra you should update to use that repository instead.

  • The qiskit.util module has been deprecated and will be removed in a future release. It has been replaced by qiskit.utils which provides the same functionality and will be expanded in the future. Note that no DeprecationWarning will be emitted regarding this deprecation since it was not feasible on Python 3.6.

  • The CXDirection transpiler pass in the qiskit.transpiler.passes module has been deprecated and will be removed in a future release. Instead the GateDirection should be used. It behaves identically to the CXDirection except that it now also supports transforming a circuit with ECRGate gates in addition to CXGate gates.

  • The CheckCXDirection transpiler pass in the qiskit.transpiler.passes module has been deprecated and will be removed in a future release. Instead the CheckGateDirection pass should be used. It behaves identically to the CheckCXDirection except that it now also supports checking the direction of all 2-qubit gates, not just CXGate gates.

  • The WeightedAdder method num_ancilla_qubits() is deprecated and will be removed in a future release. It has been replaced with the qiskit.circuit.library.WeightedAdder.num_ancillas attribute which is consistent with other circuit libraries’ APIs.

  • The following legacy methods of the qiskit.quantum_info.Pauli class have been deprecated. See the method documentation for replacement use in the updated Pauli class.

    • from_label()
    • sgn_prod()
    • to_spmatrix()
    • kron()
    • update_z()
    • update_x()
    • insert_paulis()
    • append_paulis()
    • delete_qubits()
    • pauli_single()
    • random()
  • Using a list or numpy.ndarray as the channel or target argument for the qiskit.quantum_info.process_fidelity(), qiskit.quantum_info.average_gate_fidelity(), qiskit.quantum_info.gate_error(), and qiskit.quantum_info.diamond_norm() functions has been deprecated and will not be supported in a future release. The inputs should instead be a Gate or a BaseOperator subclass object (eg. Operator, Choi, etc.)

  • Accessing references from Qubit and Clbit instances to their containing registers via the register or index properties has been deprecated and will be removed in a future release. Instead, Register objects can be queried to find the Bit objects they contain.

  • The current functionality of the qiskit.visualization.pulse_drawer() function is deprecated and will be replaced by qiskit.visualization.pulse_drawer_v2() (which is not backwards compatible) in a future release.

  • The use of methods inherited from the set type on the output of the parameters attribute (which used to be a set) of the QuantumCircuit class are deprecated and will be removed in a future release. This includes the methods from the add(), difference(), difference_update(), discard(), intersection(), intersection_update(), issubset(), issuperset(), symmetric_difference(), symmetric_difference_update(), union(), update(), __isub__() (which is the -= operator), and __ixor__() (which is the ^= operator).

  • The name of the first (and only) positional argument for the qiskit.circuit.QuantumCircuit.bind_parameters() method has changed from value_dict to values. The passing an argument in with the name values_dict is deprecated and will be removed in future release. For example, if you were previously calling bind_parameters() with a call like: bind_parameters(values_dict={}) this is deprecated and should be replaced by bind_parameters(values={}) or even better just pass the argument positionally bind_parameters({}).

  • The name of the first (and only) positional argument for the qiskit.circuit.QuantumCircuit.assign_parameters() method has changed from param_dict to parameters. Passing an argument in with the name param_dict is deprecated and will be removed in future release. For example, if you were previously calling assign_parameters() with a call like: assign_parameters(param_dict={}) this is deprecated and should be replaced by assign_parameters(values={}) or even better just pass the argument positionally assign_parameters({}).

Bug Fixes

Other Notes

  • The snapshots of all the fake/mock backends in qiskit.test.mock have been updated to reflect recent device changes. This includes a change in the basis_gates attribute for the BackendConfiguration to ['cx', 'rz', 'sx', 'x', 'id'], the addition of a readout_length property to the qubit properties in the BackendProperties, and updating the PulseDefaults so that all the mock backends support parametric pulse based InstructionScheduleMap instances.

Aer 0.8.0

Prelude

The 0.8 release includes several new features and bug fixes. The highlights for this release are: the introduction of a unified AerSimulator backend for running circuit simulations using any of the supported simulation methods; a simulator instruction library (qiskit.providers.aer.library) which includes custom instructions for saving various kinds of simulator data; MPI support for running large simulations on a distributed computing environment.

New Features

  • Python 3.9 support has been added in this release. You can now run Qiskit Aer using Python 3.9 without building from source.

  • Add the CMake flag DISABLE_CONAN (default=``OFF``)s. When installing from source, setting this to ON allows bypassing the Conan package manager to find libraries that are already installed on your system. This is also available as an environment variable DISABLE_CONAN, which takes precedence over the CMake flag. This is not the official procedure to build AER. Thus, the user is responsible of providing all needed libraries and corresponding files to make them findable to CMake.

  • This release includes support for building qiskit-aer with MPI support to run large simulations on a distributed computing environment. See the contributing guide(opens in a new tab) for instructions on building and running in an MPI environment.

  • It is now possible to build qiskit-aer with CUDA enabled in Windows. See the contributing guide(opens in a new tab) for instructions on building from source with GPU support.

  • When building the qiskit-aer Python extension from source several build dependencies need to be pre-installed to enable C++ compilation. As a user convenience when building the extension any of these build dependencies which were missing would be automatically installed using pip prior to the normal setuptools installation steps, however it was previously was not possible to avoid this automatic installation. To solve this issue a new environment variable DISABLE_DEPENDENCY_INSTALL has been added. If it is set to 1 or ON when building the python extension from source this will disable the automatic installation of these missing build dependencies.

  • Adds support for optimized N-qubit Pauli gate ( qiskit.circuit.library.PauliGate) to the StatevectorSimulator, UnitarySimulator, and the statevector and density matrix methods of the QasmSimulator and AerSimulator.

  • The run() method for the AerSimulator, QasmSimulator, StatevectorSimulator, and UnitarySimulator backends now takes a QuantumCircuit (or a list of QuantumCircuit objects) as it’s input. The previous QasmQobj object is still supported for now, but will be deprecated in a future release.

    For an example of how to use this see:

    from qiskit import transpile, QuantumCircuit
     
    from qiskit.providers.aer import Aer
     
    backend = Aer.get_backend('aer_simulator')
     
    circuit = QuantumCircuit(2)
    qc.h(0)
    qc.cx(0, 1)
    qc.measure_all()
     
    tqc = transpile(circuit, backend)
    result = backend.run(tqc, shots=4096).result()
  • The run() method for the PulseSimulator backend now takes a Schedule (or a list of Schedule objects) as it’s input. The previous PulseQobj object is still supported for now, but will be deprecated in a future release.

  • Adds the new AerSimulator simulator backend supporting the following simulation methods

    • automatic
    • statevector
    • stabilizer
    • density_matrix
    • matrix_product_state
    • unitary
    • superop

    The default automatic method will automatically choose a simulation method separately for each run circuit based on the circuit instructions and noise model (if any). Initializing a simulator with a specific method can be done using the method option.

    GPU simulation for the statevector, density matrix and unitary methods can be enabled by setting the device='GPU' backend option.

    Note that the unitary and superop methods do not support measurement as they simulate the unitary matrix or superoperator matrix of the run circuit so one of the new save_unitary(), save_superop(), or save_state() instructions must be used to save the simulator state to the returned results. Similarly state of the other simulations methods can be saved using the appropriate instructions. See the qiskit.providers.aer.library API documents for more details.

    Note that the AerSimulator simulator superceds the QasmSimulator, StatevectorSimulator, and UnitarySimulator backends which will be deprecated in a future release.

  • Updates the AerProvider class to include multiple AerSimulator backends preconfigured for all available simulation methods and simulation devices. The new backends can be accessed through the provider interface using the names

    • "aer_simulator"
    • "aer_simulator_statevector"
    • "aer_simulator_stabilizer"
    • "aer_simulator_density_matrix"
    • "aer_simulator_matrix_product_state"
    • "aer_simulator_extended_stabilizer"
    • "aer_simulator_unitary"
    • "aer_simulator_superop"

    Additional if Aer was installed with GPU support on a compatible system the following GPU backends will also be available

    • "aer_simulator_statevector_gpu"
    • "aer_simulator_density_matrix_gpu"
    • "aer_simulator_unitary_gpu"

    For example:

    from qiskit import Aer
     
    # Get the GPU statevector simulator backend
    backend = Aer.get_backend('aer_simulator_statevector_gpu')
  • Added a new norm estimation method for performing measurements when using the "extended_stabilizer" simulation method. This norm estimation method can be used by passing the following options to the AerSimulator and QasmSimulator backends

    simulator = QasmSimulator(
        method='extended_stabilizer',
        extended_stabilizer_sampling_method='norm_estimation')

    The norm estimation method is slower than the alternative metropolis or resampled_metropolis options, but gives better performance on circuits with sparse output distributions. See the documentation of the QasmSimulator for more information.

  • Adds instructions for saving the state of the simulator in various formats. These instructions are

    • qiskit.providers.aer.library.SaveDensityMatrix
    • qiskit.providers.aer.library.SaveMatrixProductState
    • qiskit.providers.aer.library.SaveStabilizer
    • qiskit.providers.aer.library.SaveState
    • qiskit.providers.aer.library.SaveStatevector
    • qiskit.providers.aer.library.SaveStatevectorDict
    • qiskit.providers.aer.library.SaveUnitary

    These instructions can be appended to a quantum circuit by using the save_density_matrix, save_matrix_product_state, save_stabilizer, save_state, save_statevector, save_statevector_dict, save_unitary circuit methods which are added to QuantumCircuit when importing Aer.

    See the qiskit.providers.aer.library API documentation for details on method compatibility for each instruction.

    Note that the snapshot instructions SnapshotStatevector, SnapshotDensityMatrix, SnapshotStabilizer are still supported but will be deprecated in a future release.

  • Adds qiskit.providers.aer.library.SaveExpectationValue and qiskit.providers.aer.library.SaveExpectationValueVariance quantum circuit instructions for saving the expectation value H=Tr[Hρ]\langle H\rangle = Tr[H\rho], or expectation value and variance Var(H)=H2H2Var(H) = \langle H^2\rangle - \langle H\rangle^2, of a Hermitian operator HH for the simulator state ρ\rho. These instruction can be appended to a quantum circuit by using the save_expectation_value and save_expectation_value_variance circuit methods which is added to QuantumCircuit when importing Aer.

    Note that the snapshot instruction SnapshotExpectationValue, is still supported but will be deprecated in a future release.

  • Adds qiskit.providers.aer.library.SaveProbabilities and qiskit.providers.aer.library.SaveProbabilitiesDict quantum circuit instruction for saving all measurement outcome probabilities for Z-basis measurements of the simualtor state. These instruction can be appended to a quantum circuit by using the save_probabilities and save_probabilities_dict circuit methods which is added to QuantumCircuit when importing Aer.

    Note that the snapshot instruction SnapshotProbabilities, is still supported but will be deprecated in a future release.

  • Adds qiskit.providers.aer.library.SaveAmplitudes and qiskit.providers.aer.library.SaveAmplitudesSquared circuit instructions for saving select complex statevector amplitudes, or select probabilities (amplitudes squared) for supported simulation methods. These instructions can be appended to a quantum circuit by using the save_amplitudes and save_amplitudes_squared circuit methods which is added to QuantumCircuit when importing Aer.

  • Adds instructions for setting the state of the simulators. These instructions must be defined on the full number of qubits in the circuit. They can be applied at any point in a circuit and will override the simulator state with the one specified. Added instructions are

    • qiskit.providers.aer.library.SetDensityMatrix
    • qiskit.providers.aer.library.SetStabilizer
    • qiskit.providers.aer.library.SetStatevector
    • qiskit.providers.aer.library.SetUnitary

    These instruction can be appended to a quantum circuit by using the set_density_matrix, set_stabilizer, set_statevector, set_unitary circuit methods which are added to QuantumCircuit when importing Aer.

    See the qiskit.providers.aer.library API documentation for details on method compatibility for each instruction.

  • Added support for diagonal gates to the "matrix_product_state" simulation method.

  • Added support for the initialize instruction to the "matrix_product_state" simulation method.

Known Issues

  • There is a known issue where the simulation of certain circuits with a Kraus noise model using the "matrix_product_state" simulation method can cause the simulator to crash. Refer to #306(opens in a new tab) for more information.

Upgrade Notes

  • The minimum version of Conan(opens in a new tab) has been increased to 1.31.2. This was necessary to fix a compatibility issue with newer versions of the urllib3(opens in a new tab) (which is a dependency of Conan). It also adds native support for AppleClang 12 which is useful for users with new Apple computers.

  • pybind11 minimum version required is 2.6 instead of 2.4. This is needed in order to support CUDA enabled compilation in Windows.

  • Cython has been removed as a build dependency.

  • Removed x90 gate decomposition from noise models that was deprecated in qiskit-aer 0.7. This decomposition is now done by using regular noise model basis gates and the qiskit transpiler.

  • The following options for the "extended_stabilizer" simulation method have changed.

    • extended_stabilizer_measure_sampling: This option has been replaced by the options extended_stabilizer_sampling_method, which controls how we simulate qubit measurement.
    • extended_stabilizer_mixing_time: This option has been renamed as extended_stabilizer_metropolis_mixing_time to clarify it only applies to the metropolis and resampled_metropolis sampling methods.
    • extended_stabilizer_norm_estimation_samples: This option has been renamed to extended_stabilizer_norm_estimation_default_samples.

    One additional option, extended_stabilizer_norm_estimation_repetitions has been added, whih controls part of the behaviour of the norm estimation sampling method.

Deprecation Notes

  • Python 3.6 support has been deprecated and will be removed in a future release. When support is removed you will need to upgrade the Python version you’re using to Python 3.7 or above.

Bug Fixes

  • Fixes bug with AerProvider where options set on the returned backends using set_options() were stored in the provider and would persist for subsequent calls to get_backend() for the same named backend. Now every call to and backends() returns a new instance of the simulator backend that can be configured.
  • Fixes bug in the error message returned when a circuit contains unsupported simulator instructions. Previously some supported instructions were also being listed in the error message along with the unsupported instructions.
  • Fixes issue with setting QasmSimulator basis gates when using "method" and "noise_model" options together, and when using them with a simulator constructed using from_backend(). Now the listed basis gates will be the intersection of gates supported by the backend configuration, simulation method, and noise model basis gates. If the intersection of the noise model basis gates and simulator basis gates is empty a warning will be logged.
  • Fix bug where the "sx"` gate SXGate was not listed as a supported gate in the C++ code, in StateOpSet of matrix_product_state.hp.
  • Fix bug where "csx", "cu2", "cu3" were incorrectly listed as supported basis gates for the "density_matrix" method of the QasmSimulator.
  • Fix bug where parameters were passed incorrectly between functions in matrix_product_state_internal.cpp, causing wrong simulation, as well as reaching invalid states, which in turn caused an infinite loop.
  • Fixes a bug that resulted in c_if not working when the width of the conditional register was greater than 64. See #1077(opens in a new tab).
  • Fixes a bug #1153(opens in a new tab)) where noise on conditional gates was always being applied regardless of whether the conditional gate was actually applied based on the classical register value. Now noise on a conditional gate will only be applied in the case where the conditional gate is applied.
  • Fixes a bug with nested OpenMP flag was being set to true when it shouldn’t be.
  • Fixes a bug when applying truncation in the matrix product state method of the QasmSimulator.
  • Fixed issue #1126(opens in a new tab): bug in reporting measurement of a single qubit. The bug occured when copying the measured value to the output data structure.
  • In MPS, apply_kraus was operating directly on the input bits in the parameter qubits, instead of on the internal qubits. In the MPS algorithm, the qubits are constantly moving around so all operations should be applied to the internal qubits.
  • When invoking MPS::sample_measure, we need to first sort the qubits to the default ordering because this is the assumption in qasm_controller.This is done by invoking the method move_all_qubits_to_sorted_ordering. It was correct in sample_measure_using_apply_measure, but missing in sample_measure_using_probabilities.
  • Fixes bug with the from_backend() method of the QasmSimulator that would set the local attribute of the configuration to the backend value rather than always being set to True.
  • Fixes bug in from_backend() and from_backend() where basis_gates was set incorrectly for IBMQ devices with basis gate set ['id', 'rz', 'sx', 'x', 'cx']. Now the noise model will always have the same basis gates as the backend basis gates regardless of whether those instructions have errors in the noise model or not.
  • Fixes an issue where the Extended “extended_stabilizer” simulation method would give incorrect results on quantum circuits with sparse output distributions. Refer to #306(opens in a new tab) for more information and examples.

Ignis 0.6.0

New Features

  • The qiskit.ignis.mitigation.expval_meas_mitigator_circuits() function has been improved so that the number of circuits generated by the function used for calibration by the CTMP method are reduced from O(n)O(n) to O(logn)O(\log{n}) (where nn is the number of qubits).

Upgrade Notes

  • The qiskit.ignis.verification.randomized_benchmarking_seq() function is now using the upgraded CNOTDihedral class, qiskit.ignis.verification.CNOTDihedral, which enables performing CNOT-Dihedral Randomized Benchmarking on more than two qubits.
  • The python package retworkx is now a requirement for installing qiskit-ignis. It replaces the previous usage of networkx (which is no longer a requirement) to get better performance.
  • The scikit-learn dependency is no longer required and is now an optional requirement. If you’re using the IQ measurement discriminators (IQDiscriminationFitter, LinearIQDiscriminationFitter, QuadraticIQDiscriminationFitter, or SklearnIQDiscriminator) you will now need to manually install scikit-learn, either by running pip install scikit-learn or when you’re also installing qiskit-ignis with pip install qiskit-ignis[iq].

Bug Fixes

  • Fixed an issue in the expectation value method expectation_value(), for the error mitigation classes TensoredExpvalMeasMitigator and CTMPExpvalMeasMitigator if the qubits kwarg was not specified it would incorrectly use the total number of qubits of the mitigator, rather than the number of classical bits in the count dictionary leading to greatly reduced performance. Fixed #561(opens in a new tab)
  • Fix the "auto" method of the TomographyFitter, StateTomographyFitter, and ProcessTomographyFitter to only use "cvx" if CVXPY is installed and a third-party SDP solver other than SCS is available. This is because the SCS solver has lower accuracy than other solver methods and often returns a density matrix or Choi-matrix that is not completely-positive and fails validation when used with the qiskit.quantum_info.state_fidelity() or qiskit.quantum_info.process_fidelity() functions.

Aqua 0.9.0

This release officially deprecates the Qiskit Aqua project, in the future (no sooner than 3 months from this release) the Aqua project will have it’s final release and be archived. All the functionality that qiskit-aqua provides has been migrated to either new packages or to other qiskit packages. The application modules that are provided by qiskit-aqua have been split into several new packages: qiskit-optimization, qiskit-nature, qiskit-machine-learning, and qiskit-finance. These packages can be installed by themselves (via the standard pip install command, ie pip install qiskit-nature) or with the rest of the Qiskit metapackage as optional extras (ie, pip install 'qiskit[finance,optimization]' or pip install 'qiskit[all]'. The core building blocks for algorithms and the operator flow now exist as part of qiskit-terra at qiskit.algorithms and qiskit.opflow. Depending on your existing usage of Aqua you should either use the application packages or the new modules in Qiskit Terra.

For more details on how to migrate from using Qiskit Aqua, you can refer to the migration guide(opens in a new tab).

IBM Q Provider 0.12.2

No change

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