Qiskit 0.35 release notes

0.35.0

Terra 0.20.0

Prelude

The Qiskit Terra 0.20.0 release highlights are:

The introduction of multithreaded modules written in Rust to accelerate the performance of certain portions of Qiskit Terra and improve scaling with larger numbers of qubits. However, when building Qiskit from source a Rust(opens in a new tab) compiler is now required.
More native support for working with a Target in the transpiler. Several passes now support working directly with a Target object which makes the transpiler robust in the types of backends it can target.
The introduction of the qiskit.primitives module. These APIs provide different abstraction levels for computing outputs of interest from QuantumCircuit and using backends. For example, the BaseEstimator defines an abstract interface for estimating an expectation value of an observable. This can then be used to construct higher level algorithms and applications that are built using the estimation of expectation values without having to worry about the implementation of computing the expectation value. This decoupling allows the implementation to improve in speed and quality while adhering to the defined abstract interface. Likewise, the BaseSampler computes quasi-probability distributions from circuit measurements. Other primitives will be introduced in the future.

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

New Features

Added a new constructor method for the Operator class, Operator.from_circuit() for creating a new Operator object from a QuantumCircuit. While this was possible normally using the default constructor, the Operator.from_circuit() method provides additional options to adjust how the operator is created. Primarily this lets you permute the qubit order based on a set Layout. For, example:
```
from qiskit.circuit import QuantumCircuit
from qiskit import transpile
from qiskit.transpiler import CouplingMap
from qiskit.quantum_info import Operator
 
circuit = QuantumCircuit(3)
circuit.h(0)
circuit.cx(0, 1)
circuit.cx(1, 2)
 
cmap = CouplingMap.from_line(3)
out_circuit = transpile(circuit, initial_layout=[2, 1, 0], coupling_map=cmap)
operator = Operator.from_circuit(out_circuit)
```
the operator variable will have the qubits permuted based on the layout so that it is identical to what is returned by Operator(circuit) before transpilation.
Added a new method DAGCircuit.copy_empty_like() to the DAGCircuit class. This method is used to create a new copy of an existing DAGCircuit object with the same structure but empty of any instructions. This method is the same as the private method _copy_circuit_metadata(), but instead is now part of the public API of the class.
The fake backend and fake provider classes which were previously available in qiskit.test.mock are now also accessible in a new module: qiskit.providers.fake_provider. This new module supersedes the previous module qiskit.test.mock which will be deprecated in Qiskit 0.21.0.
Added a new gate class, LinearFunction, that efficiently encodes a linear function (i.e. a function that can be represented by a sequence of CXGate and SwapGate gates).
Added a new transpiler pass CollectLinearFunctions that collects blocks of consecutive CXGate and SwapGate gates in a circuit, and replaces each block with a LinearFunction gate.
Added a new transpiler pass LinearFunctionsSynthesis that synthesizes any LinearFunction gates in using the Patel-Markov-Hayes algorithm(opens in a new tab). When combined with the CollectLinearFunctions transpiler pass this enables to collect blocks of consecutive CXGate and SwapGate gates in a circuit, and re-synthesize them using the Patel-Markov-Hayes algorithm(opens in a new tab).
Added a new transpiler pass LinearFunctionsToPermutations that replaces a LinearFunction gate by a Permutation circuit whenever possible.

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

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

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

The constructors for the ZFeatureMap and ZZFeatureMap classes have a new keyword argument parameter_prefix. This new argument is used to set the prefix of parameters of the data encoding circuit. For example:
```
from qiskit.circuit.library import ZFeatureMap
 
feature_map = ZFeatureMap(feature_dimension=4, parameter_prefix="my_prefix")
feature_map.decompose().draw('mpl')
```
the generated ZFeatureMap circuit has prefixed all its internal parameters with the prefix "my_prefix".

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

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

outputs

Original circuit:
 
q_0: ──■──────────────■──
     ┌─┴─┐┌────────┐┌─┴─┐
q_1: ┤ X ├┤ P(2*φ) ├┤ X ├
     └───┘└────────┘└───┘
ZX based circuit:
                                         ┌─────────────┐            »
q_0: ────────────────────────────────────┤0            ├────────────»
     ┌──────────┐┌──────────┐┌──────────┐│  Rzx(2.0*φ) │┌──────────┐»
q_1: ┤ Rz(-π/2) ├┤ Rx(-π/2) ├┤ Rz(-π/2) ├┤1            ├┤ Rx(-2*φ) ├»
     └──────────┘└──────────┘└──────────┘└─────────────┘└──────────┘»
«
«q_0: ────────────────────────────────────────────────
«     ┌──────────┐┌──────────┐┌──────────┐┌──────────┐
«q_1: ┤ Rz(-π/2) ├┤ Rx(-π/2) ├┤ Rz(-π/2) ├┤ P(2.0*φ) ├
«     └──────────┘└──────────┘└──────────┘└──────────┘
Duration of schedule with the calibration:
1600
Duration of standard with two CNOT gates:
6848

The DAGOpNode, DAGInNode and DAGOutNode classes now define a custom __repr__ method which outputs a representation. Per the Python documentation(opens in a new tab) the output is a string representation that is roughly equivalent to the Python string used to create an equivalent object.
The performance of the SparsePauliOp.simplify() method has greatly improved by replacing the use of numpy.unique to compute unique elements of an array by a new similar function implemented in Rust that doesn’t pre-sort the array.

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

op = SparsePauliOp.from_list([("X", 1), ("Y", 1)])
op2 = SparsePauliOp.from_list([("X", 1), ("Y", 1), ("Z", 0)])
op3 = SparsePauliOp.from_list([("Y", 1), ("X", 1)])
 
print(op == op2)  # False
print(op == op3)  # False
print(op.equiv(op2))  # True
print(op.equiv(op3))  # True

Added new fake backend classes from snapshots of the IBM Quantum systems based on the BackendV2 interface and provided a Target for each backend. BackendV2 based versions of all the existing backends are added except for three old backends FakeRueschlikon, FakeTenerife and FakeTokyo as they do not have snapshots files available which are required for creating a new fake backend class based on BackendV2.

These new V2 fake backends will enable testing and development of new features introduced by BackendV2 and Target such as improving the transpiler.
Added a new gate class XXMinusYYGate to the circuit library (qiskit.circuit.library) for the XX-YY interaction. This gate can be used to implement the bSwap gate(opens in a new tab) and its powers. It also arises in the simulation of superconducting fermionic models.
Added new gate class, XXPlusYYGate, to the circuit library (qiskit.circuit.library). This gate is a 2-qubit parameterized XX+YY interaction, also known as an XY gate, and is based on the gate described in https://arxiv.org/abs/1912.04424(opens in a new tab).
The FakeBogota, FakeManila, FakeRome, and FakeSantiago fake backends which can be found in the qiskit.providers.fake_provider module can now be used as backends in Pulse experiments as they now include a PulseDefaults created from a snapshot of the equivalent IBM Quantum machine’s properties.
The ConsolidateBlocks pass has a new keyword argument on its constructor, target. This argument is used to specify a Target object representing the compilation target for the pass. If it is specified it supersedes the basis_gates kwarg. If a target is specified, the pass will respect the gates and qubits for the instructions defined in the Target when deciding which gates to consolidate into a unitary.
The Target class has a new method, instruction_supported() which is used to query the target to see if an instruction (the combination of an operation and the qubit(s) it is executed on) is supported on the backend modelled by the Target.

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

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

from qiskit.qpy import dump, load
from qiskit.circuit import QuantumCircuit, Parameter
import json
import io
 
class CustomObject:
    """Custom string container object."""
 
    def __init__(self, string):
        self.string = string
 
    def __eq__(self, other):
        return self.string == other.string
 
class CustomSerializer(json.JSONEncoder):
    """Custom json encoder to handle CustomObject."""
 
    def default(self, o):
        if isinstance(o, CustomObject):
            return {"__type__": "Custom", "value": o.string}
        return json.JSONEncoder.default(self, o)
 
class CustomDeserializer(json.JSONDecoder):
    """Custom json decoder to handle CustomObject."""
 
    def __init__(self, *args, **kwargs):
        super().__init__(*args, object_hook=self.object_hook, **kwargs)
 
    def object_hook(self, o):
        """Hook to override default decoder."""
        if "__type__" in o:
            obj_type = o["__type__"]
            if obj_type == "Custom":
                return CustomObject(o["value"])
        return o
 
theta = Parameter("theta")
qc = QuantumCircuit(2, global_phase=theta)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()
circuits = [qc, qc.copy()]
circuits[0].metadata = {"key": CustomObject("Circuit 1")}
circuits[1].metadata = {"key": CustomObject("Circuit 2")}
with io.BytesIO() as qpy_buf:
    dump(circuits, qpy_buf, metadata_serializer=CustomSerializer)
    qpy_buf.seek(0)
    new_circuits = load(qpy_buf, metadata_deserializer=CustomDeserializer)

The DenseLayout pass has a new keyword argument on its constructor, target. This argument is used to specify a Target object representing the compilation target for the pass. If it is specified it supersedes the other arguments on the constructor, coupling_map and backend_prop.
The Target class has a new method, operation_names_for_qargs(). This method is used to get the operation names (i.e. lookup key in the target) for the operations on a given qargs tuple.
A new pass DynamicalDecouplingPadding has been added to the qiskit.transpiler.passes module. This new pass supersedes the existing DynamicalDecoupling pass to work with the new scheduling workflow in the transpiler. It is a subclass of the BasePadding pass and depends on having scheduling and alignment analysis passes run prior to it in a PassManager. This new pass can take a pulse_alignment argument which represents a hardware constraint for waveform start timing. The spacing between gates comprising a dynamical decoupling sequence is now adjusted to satisfy this constraint so that the circuit can be executed on hardware with the constraint. This value is usually found in BackendConfiguration.timing_constraints. Additionally the pass also has an extra_slack_distribution option has been to control how to distribute the extra slack when the duration of the created dynamical decoupling sequence is shorter than the idle time of your circuit that you want to fill with the sequence. This defaults to middle which is identical to conventional behavior. The new strategy split_edges evenly divide the extra slack into the beginning and end of the sequence, rather than adding it to the interval in the middle of the sequence. This might result in better noise cancellation especially when pulse_alignment > 1.

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

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

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

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

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

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

>>> op = SparsePauliOp(["X", "X"], coeffs=[1+1e-17j, 1e-17+1j)
>>> op.chop()
SparsePauliOp(['X', 'X'],
              coeffs=[1.+0.j, 0.+1.j])

Added a new kwarg, target, to the constructor for the GatesInBasis transpiler pass. This new argument can be used to optionally specify a Target object that represents the backend. When set this Target will be used for determining whether a DAGCircuit contains gates outside the basis set and the basis_gates argument will not be used.
Added partial support for running on ppc64le and s390x Linux platforms. This release will start publishing pre-compiled binaries for ppc64le and s390x Linux platforms on all Python versions. However, unlike other supported platforms not all of Qiskit’s upstream dependencies support these platforms yet. So a C/C++ compiler may be required to build and install these dependencies and a simple pip install qiskit-terra with just a working Python environment will not be sufficient to install Qiskit. Additionally, these same constraints prevent us from testing the pre-compiled wheels before publishing them, so the same guarantees around platform support that exist for the other platforms don’t apply here.
The Gradient and QFI classes can now calculate the imaginary part of expectation value gradients. When using a different measurement basis, i.e. -Y instead of Z, we can measure the imaginary part of gradients The measurement basis can be set with the aux_meas_op argument.

For the gradients, aux_meas_op = Z computes 0.5Re[(⟨ψ(ω)|)O(θ)|dωψ(ω)〉] and aux_meas_op = -Y computes 0.5Im[(⟨ψ(ω)|)O(θ)|dωψ(ω)〉]. For the QFIs, aux_meas_op = Z computes 4Re[(dω⟨<ψ(ω)|)(dω|ψ(ω)〉)] and aux_meas_op = -Y computes 4Im[(dω⟨<ψ(ω)|)(dω|ψ(ω)〉)]. For example:
```
from qiskit import QuantumRegister, QuantumCircuit
from qiskit.opflow import CircuitStateFn, Y
from qiskit.opflow.gradients.circuit_gradients import LinComb
from qiskit.circuit import Parameter
 
a = Parameter("a")
b = Parameter("b")
params = [a, b]
 
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(params[0], q[0])
qc.rx(params[1], q[0])
op = CircuitStateFn(primitive=qc, coeff=1.0)
 
aux_meas_op = -Y
 
prob_grad = LinComb(aux_meas_op=aux_meas_op).convert(operator=op, params=params)
```
The InstructionDurations class now has support for working with parameters of an instruction. Each entry in an InstructionDurations object now consists of a tuple of (inst_name, qubits, duration, parameters, unit). This enables an InstructionDurations to define durations for an instruction given a certain parameter value to account for different durations with different parameter values on an instruction that takes a numeric parameter.
Added a new value for the style keyword argument on the circuit drawer function circuit_drawer() and QuantumCircuit.draw() method, iqx_dark. When style is set to iqx_dark with the mpl drawer backend, the output visualization will use a color scheme similar to the the dark mode color scheme used by the IBM Quantum composer. For example:
```
from qiskit.circuit import QuantumCircuit
from matplotlib.pyplot import show
 
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.p(0.2, 1)
 
circuit.draw("mpl", style="iqx-dark")
```
Several lazy dependency checkers have been added to the new module qiskit.utils.optionals, which can be used to query if certain Qiskit functionality is available. For example, you can ask if Qiskit has detected the presence of matplotlib by asking if qiskit.utils.optionals.HAS_MATPLOTLIB. These objects only attempt to import their dependencies when they are queried, so you can use them in runtime code without affecting import time.
Import time for qiskit has been significantly improved, especially for those with many of Qiskit Terra’s optional dependencies installed.

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

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

The output is:

['0x0', '0x1', '0x2', '0x3', '0x4', '0x5', '0x6', '0x7']
['0x0', '0x1', '0x0', '0x1', '0x0', '0x1', '0x0', '0x1']

The internals of the StochasticSwap algorithm have been reimplemented to be multithreaded and are now written in the Rust(opens in a new tab) programming language instead of Cython. This significantly increases the run time performance of the compiler pass and by extension transpile() when run with optimization_level 0, 1, and 2. By default the pass will use up to the number of logical CPUs on your local system but you can control the number of threads used by the pass by setting the RAYON_NUM_THREADS environment variable to an integer value. For example, setting RAYON_NUM_THREADS=4 will run the StochasticSwap with 4 threads.
A new environment variable QISKIT_FORCE_THREADS is available for users to directly control whether potentially multithreaded portions of Qiskit’s code will run in multiple threads. Currently this is only used by the StochasticSwap transpiler pass but it likely will be used other parts of Qiskit in the future. When this env variable is set to TRUE any multithreaded code in Qiskit Terra will always use multiple threads regardless of any other runtime conditions that might have otherwise caused the function to use a single threaded variant. For example, in StochasticSwap if the pass is being run as part of a transpile() call with > 1 circuit that is being executed in parallel with multiprocessing via parallel_map() the StochasticSwap will not use multiple threads to avoid potentially oversubscribing CPU resources. However, if you’d like to use multiple threads in the pass along with multiple processes you can set QISKIT_FORCE_THREADS=TRUE.
New fake backend classes are available under qiskit.providers.fake_provider. These include mocked versions of ibm_cairo, ibm_hanoi, ibmq_kolkata, ibm_nairobi, and ibm_washington. As with the other fake backends, these include snapshots of calibration and error data taken from the real system, and can be used for local testing, compilation and simulation.

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

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

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

The output is as:

     ┌─────────────────────────────────────┐
q_0: ┤ State Preparation(0.70711,-0.70711) ├
     └─────────────────────────────────────┘

The Optimize1qGates transpiler pass now has support for optimizing U1Gate, U2Gate, and PhaseGate gates with unbound parameters in a circuit. Previously, if these gates had unbound parameters the pass would not use them. For example:

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

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

qc = QuantumCircuit(1)
qc.u1(2*phi + alpha + 0.3, 0)

The methods Pauli.evolve() and PauliList.evolve() now have a new keyword argument, frame, which is used to perform an evolution of a Pauli by a Clifford. If frame='h' (default) then it does the Heisenberg picture evolution of a Pauli by a Clifford ( $P' = C^\dagger P C$ ), and if frame='s' then it does the Schrödinger picture evolution of a Pauli by a Clifford ( $P' = C P C^\dagger$ ). The latter option yields a faster calculation, and is also useful in certain cases. This new option makes the calculation of the greedy Clifford decomposition method in decompose_clifford significantly faster.
Added a new module to Qiskit: qiskit.primitives. The primitives module is where APIs are defined which provide different abstractions around computing certain common functions from QuantumCircuit which abstracts away the details of the underlying execution on a Backend. This enables higher level algorithms and applications to concentrate on performing the computation and not need to worry about the execution and processing of results and have a standardized interface for common computations. For example, estimating an expectation value of a quantum circuit and observable can be performed by any class implementing the BaseEstimator class and consumed in a standardized manner regardless of the underlying implementation. Applications can then be written using the primitive interface directly.

To start the module contains two types of primitives, the Sampler (see BaseSampler for the abstract class definition) and Estimator (see BaseEstimator for the abstract class definition). Reference implementations are included in the qiskit.primitives module and are built using the qiskit.quantum_info module which perform ideal simulation of primitive operation. The expectation is that provider packages will offer their own implementations of these interfaces for providers which can efficiently implement the protocol natively (typically using a classical runtime). Additionally, in the future for providers which do not offer a native implementation of the primitives a method will be provided which will enable constructing primitive objects from a Backend.
Added a new module, qiskit.qpy, which contains the functionality previously exposed in qiskit.circuit.qpy_serialization. The public functions previously exposed at qiskit.circuit.qpy_serialization, dump() and load() are now available from this new module (although they are still accessible from qiskit.circuit.qpy_serialization but this will be deprecated in a future release). This new module was added in the interest of the future direction of the QPY file format, which in future versions will support representing pulse Schedule and ScheduleBlock objects in addition to the QuantumCircuit objects it supports today.
Added a new attribute, qubit_properties to the Target class. This attribute contains a list of QubitProperties objects for each qubit in the target. For example:
```
target.qubit_properties[2]
```
will contain the QubitProperties for qubit number 2 in the target.

For BackendV2 authors, if you were previously defining QubitProperties directly on your BackendV2 implementation by overriding BackendV2.qubit_properties() this will still work fine. However, if you do move the definition to the underlying Target object and remove the specialized BackendV2.qubit_properties() implementation which will enable using qubit properties in the transpiler and also maintain API compatibility with your previous implementation.
Added a new function, qiskit.algorithms.eval_observables(), which is used to evaluate observables given a bound QuantumCircuit. It originates from a private method, _eval_aux_ops(), of the qiskit.algorithms.VQE class but the new eval_observables() function is now more general so that it can be used in other algorithms, for example time evolution algorithms.
The basis search strategy in BasisTranslator transpiler pass has been modified into a variant of Dijkstra search which greatly improves the runtime performance of the pass when attempting to target an unreachable basis.
The DenseLayout transpiler pass is now multithreaded, which greatly improves the runtime performance of the pass. By default, it will use the number of logical CPUs on your local system, but you can control the number of threads used by the pass by setting the RAYON_NUM_THREADS environment variable to an integer value. For example, setting RAYON_NUM_THREADS=4 will run the DenseLayout pass with 4 threads.
The internal computations of Statevector.expectation_value() and DensityMatrix.expectation_value() methods have been reimplemented in the Rust programming language. This new implementation is multithreaded and by default for a Statevector or DensityMatrix >= 19 qubits will spawn a thread pool with the number of logical CPUs available on the local system. You can you can control the number of threads used by setting the RAYON_NUM_THREADS environment variable to an integer value. For example, setting RAYON_NUM_THREADS=4 will only use 4 threads in the thread pool.
Added a new SparsePauliOp.from_sparse_list() constructor that takes an iterable, where the elements represent Pauli terms that are themselves sparse, so that "XIIIIIIIIIIIIIIIX" can now be written as ("XX", [0, 16]). For example, the operator
$H = X_0 Z_3 + 2 Y_1 Y_4$
can now be constructed as
```
op = SparsePauliOp.from_sparse_list([("XZ", [0, 3], 1), ("YY", [1, 4], 2)], num_qubits=5)
# or equivalently, as previously
op = SparsePauliOp.from_list([("IZIIX", 1), ("YIIYI", 2)])
```
This facilitates the construction of very sparse operators on many qubits, as is often the case for Ising Hamiltonians.
The UnitarySynthesis transpiler pass has a new keyword argument on its constructor, target. This can be used to optionally specify a Target object which represents the compilation target for the pass. When it’s specified it will supersede the values set for basis_gates, coupling_map, and backend_props.
The UnitarySynthesisPlugin abstract plugin class has a new optional attribute implementations can add, supports_target. If a plugin has this attribute set to True a Target object will be passed in the options payload under the target field. The expectation is that this Target object will be used in place of coupling_map, gate_lengths, basis_gates, and gate_errors.
Introduced a new transpiler pass workflow for building PassManager objects for scheduling QuantumCircuit objects in the transpiler. In the new workflow scheduling and alignment passes are all AnalysisPass objects that only update the property set of the pass manager, specifically new property set item node_start_time, which holds the absolute start time of each opnode. A separate TransformationPass such as PadDelay is subsequently used to apply scheduling to the DAG. This new workflow is both more efficient and can correct for additional timing constraints exposed by a backend.

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

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

For example, if you were previously building your PassManager with something like:
```
from qiskit.transpiler import PassManager
from qiskit.transpiler.passes import TimeUnitConversion, ALAPSchedule, ValidatePulseGates, AlignMeasures
 
pm = PassManager()
scheduling = [
    ALAPSchedule(instruction_durations), PadDelay()),
    ValidatePulseGates(granularity=timing_constraints.granularity, min_length=timing_constraints.min_length),
    AlignMeasures(alignment=timing_constraints.acquire_alignment),
]
pm.append(scheduling)
```
you can instead use:
```
from qiskit.transpiler import PassManager
from qiskit.transpiler.passes import TimeUnitConversion, ALAPScheduleAnalysis, ValidatePulseGates, AlignMeasures, PadDelay
 
pm = PassManager()
scheduling = [
    ALAPScheduleAnalysis(instruction_durations), PadDelay()),
    ConstrainedReschedule(acquire_alignment=timing_constraints.acquire_alignment, pulse_alignment=timing_constraints.pulse_alignment),
    ValidatePulseGates(granularity=timing_constraints.granularity, min_length=timing_constraints.min_length),
    PadDelay()
]
pm.append(scheduling)
```
which will both be more efficient and also align instructions based on any hardware constraints.
Added a new transpiler pass ConstrainedReschedule pass. The ConstrainedReschedule pass considers both hardware alignment constraints that can be definied in a BackendConfiguration object, pulse_alignment and acquire_alignment. This new class supersedes the previosuly existing AlignMeasures as it performs the same alignment (via the property set) for measurement instructions in addition to general instruction alignment. By setting the acquire_alignment constraint argument for the ConstrainedReschedule pass it is a drop-in replacement of AlignMeasures when paired with a new BasePadding pass.
Added two new transpiler passes ALAPScheduleAnalysis and ASAPScheduleAnalysis which superscede the ALAPSchedule and ASAPSchedule as part of the reworked transpiler workflow for schedling. The new passes perform the same scheduling but in the property set and relying on a BasePadding pass to adjust the circuit based on all the scheduling alignment analysis.

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

For example, consider scheduling an input circuit like:
```
     ┌───┐┌─┐
q_0: ┤ X ├┤M├──────────────
     └───┘└╥┘   ┌───┐
q_1: ──────╫────┤ X ├──────
           ║    └─╥─┘   ┌─┐
q_2: ──────╫──────╫─────┤M├
           ║ ┌────╨────┐└╥┘
c: 1/══════╩═╡ c_0=0x1 ╞═╩═
           0 └─────────┘ 0
```
```
from qiskit import QuantumCircuit
from qiskit.transpiler import InstructionDurations, PassManager
from qiskit.transpiler.passes import ALAPScheduleAnalysis, PadDelay, SetIOLatency
from qiskit.visualization.timeline import draw
 
circuit = QuantumCircuit(3, 1)
circuit.x(0)
circuit.measure(0, 0)
circuit.x(1).c_if(0, 1)
circuit.measure(2, 0)
 
durations = InstructionDurations([("x", None, 160), ("measure", None, 800)])
 
pm = PassManager(
    [
      SetIOLatency(clbit_write_latency=800, conditional_latency=0),
      ALAPScheduleAnalysis(durations),
      PadDelay(),
    ]
)
draw(pm.run(circuit))
```
As you can see in the timeline view, the measurement on q_2 starts before the conditional X gate on the q_1, which seems to be opposite to the topological ordering of the node. This is also expected behavior because clbit write-access happens at the end edge of the measure instruction, and the read-access of the conditional gate happens the begin edge of the instruction. Thus topological ordering is preserved on the timeslot of the classical register, which is not captured by the timeline view. However, this assumes a paticular microarchitecture design, and the circuit is not necessary scheduled like this.

By using the default configuration of passes, the circuit is schedule like below.
```
from qiskit import QuantumCircuit
from qiskit.transpiler import InstructionDurations, PassManager
from qiskit.transpiler.passes import ALAPScheduleAnalysis, PadDelay
from qiskit.visualization.timeline import draw
 
circuit = QuantumCircuit(3, 1)
circuit.x(0)
circuit.measure(0, 0)
circuit.x(1).c_if(0, 1)
circuit.measure(2, 0)
 
durations = InstructionDurations([("x", None, 160), ("measure", None, 800)])
 
pm = PassManager([ALAPScheduleAnalysis(durations), PadDelay()])
draw(pm.run(circuit))
```
Note that clbit is locked throughout the measurement instruction interval. This behavior is designed based on the Qiskit Pulse, in which the acquire instruction takes AcquireChannel and MemorySlot which are not allowed to overlap with other instructions, i.e. simultaneous memory access from the different instructions is prohibited. This also always aligns the timing ordering with the topological node ordering.
Added a new transpiler pass PadDynamicalDecoupling which supersedes the DynamicalDecoupling pass as part of the reworked transpiler workflow for scheduling. This new pass will insert dynamical decoupling sequences into the circuit per any scheduling and alignment analysis that occured in earlier passes.
The plot_gate_map() visualization function and the functions built on top of it, plot_error_map() and plot_circuit_layout(), have a new keyword argument, qubit_coordinates. This argument takes a sequence of 2D coordinates to use for plotting each qubit in the backend being visualized. If specified this sequence must have a length equal to the number of qubits on the backend and it will be used instead of the default behavior.
The plot_gate_map() visualization function and the functions built on top of it, plot_error_map() and plot_circuit_layout(), now are able to plot any backend not just those with the number of qubits equal to one of the IBM backends. This relies on the retworkx spring_layout() function(opens in a new tab) to generate the layout for the visualization. If the default layout doesn’t work with a backend’s particular coupling graph you can use the qubit_coordinates function to set a custom layout.
The plot_gate_map() visualization function and the functions built on top of it, plot_error_map() and plot_circuit_layout(), are now able to function with a BackendV2 based backend. Previously, these functions only worked with BaseBackend or BackendV1 based backends.
Added a new transpiler pass, SetIOLatency. This pass takes two arguments clbit_write_latency and conditional_latency to define the I/O latency for classical bits and classical conditions on a backend. This pass will then define these values on the pass manager’s property set to enable subsequent scheduling and alignment passes to correct for these latencies and provide a more presice scheduling output of a dynamic circuit.
A new transpiler pass PadDelay has been added. This pass fills idle time on the qubit wires with Delay instructions. This pass is part of the new workflow for scheduling passes in the transpiler and depends on a scheduling analysis pass (such as ALAPScheduleAnalysis or ASAPScheduleAnalysis) and any alignment passes (such as ConstrainedReschedule) to be run prior to PadDelay.
The VF2Layout transpiler pass has a new keyword argument, target which is used to provide a Target object for the pass. When specified, the Target will be used by the pass for all information about the target device. If it is specified, the target option will take priority over the coupling_map and properties arguments.

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

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

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

from functools import partial
from scipy.optimize import minimize
 
optimizer = partial(minimize, method="L-BFGS-B")

Known Issues

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

Upgrade Notes

The classes Qubit, Clbit and AncillaQubit now have the __slots__ attribute. This is to reduce their memory usage. As a side effect, they can no longer have arbitrary data attached as attributes to them. This is very unlikely to have any effect on downstream code other than performance benefits.
The core dependency retworkx had its version requirement bumped to 0.11.0, up from 0.10.1. This improves the performance of transpilation pass ConsolidateBlocks.
The minimum supported version of symengine is now 0.9.0. This was necessary to improve compatibility with Python’s pickle module which is used internally as part of parallel dispatch with parallel_map().
The default value of QISKIT_PARALLEL when running with Python 3.9 on Linux is now set to TRUE. This means when running parallel_map() or functions that call it internally, such as transpile() and assemble(), the function will be executed in multiple processes and should have better run time performance. This change was made because the issues with reliability of parallel dispatch appear to have been resolved (see #6188(opens in a new tab) for more details). If you still encounter issues because of this you can disable multiprocessing and revert to the previous default behavior by setting the QISKIT_PARALLEL environment variable to FALSE, or setting the parallel option to False in your user config file (also please file an issue so we can track any issues related to multiprocessing).
The previously deprecated MSGate gate class previously found in qiskit.circuit.library has been removed. It was originally deprecated in the 0.16.0 release. Instead the GMS class should be used, as this allows you to create an equivalent 2 qubit MS gate in addition to an MSGate for any number of qubits.
The previously deprecated mirror() method of the Instruction class has been removed. It was originally deprecated in 0.15.0 release. Instead you should use Instruction.reverse_ops().
The previously deprecated num_ancilla_qubits() method of the qiskit.circuit.library.PiecewiseLinearPauliRotations and qiskit.circuit.library.WeightedAdder classes has been removed. It was originally deprecated in the 0.16.0 release. Instead the PiecewiseLinearPauliRotations.num_ancillas() and WeightedAdder.num_ancillas() methods should be used.
The previously deprecated reverse argument on the constructor for the PolynomialPauliRotations class has been removed. It was originally deprecated in the 0.15.0 release. Instead you should use the QuantumCircuit.reverse_bits() method to reverse the PolynomialPauliRotations circuit if needed.
The previously deprecated angle argument on the constructors for the C3SXGate and C3XGate gate classes has been removed. It was originally deprecated in the 0.17.0 release. Instead for fractional 3-controlled X gates you can use the C3XGate.power() method.
Support for using np.ndarray objects as part of the params attribute of a Gate object has been removed. This has been deprecated since Qiskit Terra 0.16.0 and now will no longer work. Instead one should create a new subclass of Gate and explicitly allow a np.ndarray input by overloading the validate_parameter() method.
A new extra csp-layout-pass has been added to the install target for pip install qiskit-terra, and is also included in the all extra. This has no effect in Qiskit Terra 0.20, but starting from Qiskit Terra 0.21, the dependencies needed only for the CSPLayout transpiler pass will be downgraded from requirements to optionals, and installed by this extra. You can prepare a package that depends on this pass by setting its requirements (or pip install command) to target qiskit-terra[csp-layout-pass].
Support for running with Python 3.6 has been removed. To run Qiskit you need a minimum Python version of 3.7.
The AmplitudeEstimator now inherits from the ABC class from the Python standard library. This requires any subclass to implement the estimate() method when previously it wasn’t required. This was done because the original intent of the class was to always be a child class of ABC, as the estimate() is required for the operation of an AmplitudeEstimator object. However, if you were previously defining an AmplitudeEstimator subclass that didn’t implement estimate() this will now result in an error.
The error raised by HoareOptimizer if the optional dependency z3 is not available has changed from TranspilerError to MissingOptionalLibraryError (which is both a QiskitError and an ImportError). This was done to be consistent with the other optional dependencies.
On Linux, the minimum library support has been raised from the manylinux2010 VM(opens in a new tab) to manylinux2014(opens in a new tab). This mirrors similar changes in Numpy and Scipy. There should be no meaningful effect for most users, unless your system still contains a very old version of glibc.
The marginal_counts() function when called with a Result object input, will now marginalize the memory field of experiment data if it’s set in the input Result. Previously, the memory field in the the input was not marginalized. This change was made because the previous behavior would result in the counts field not matching the memory field after marginal_counts() was called. If the previous behavior is desired it can be restored by setting marginalize_memory=None as an argument to marginal_counts() which will not marginalize the memory field.
The StochasticSwap transpiler pass may return different results with the same seed value set. This is due to the internal rewrite of the transpiler pass to improve runtime performance. However, this means that if you ran transpile() with optimization_level 0, 1 (the default), or 2 with a value set for seed_transpiler you may get an output with different swap mapping present after upgrading to Qiskit Terra 0.20.0.
To build Qiskit Terra from source a Rust(opens in a new tab) compiler is now needed. This is due to the internal rewrite of the StochasticSwap transpiler pass which greatly improves the runtime performance of the transpiler. The rust compiler can easily be installed using rustup, which can be found here: https://rustup.rs/(opens in a new tab)
The name attribute of the PauliEvolutionGate class has been changed to always be "PauliEvolution". This change was made to be consistent with other gates in Qiskit and enables other parts of Qiskit to quickly identify when a particular operation in a circuit is a PauliEvolutionGate. For example, it enables the unrolling to Pauli evolution gates.

Previously, the name contained the operators which are evolved, which is now available via the PauliEvolutionGate.label attribute. If a circuit with a PauliEvolutionGate is drawn, the gate will still show the same information, which gates are being evolved.
The previously deprecated methods:
- qiskit.algorithms.VQE.get_optimal_cost
- qiskit.algorithms.VQE.get_optimal_circuit
- qiskit.algorithms.VQE.get_optimal_vector
- qiskit.algorithms.VQE.optimal_params
- qiskit.algorithms.HamiltonianPhaseEstimationResult.most_likely_phase
- qiskit.algorithms.PhaseEstimationResult.most_likely_phase
which were originally deprecated in the Qiskit Terra 0.18.0 release have been removed and will no longer work.
The qiskit.algorithms.VariationalAlgorithm class is now defined as an abstract base class (ABC) which will require classes that inherit from it to define both a VariationalAlgorithm.initial_point getter and setter method.
The pass_manager kwarg for the transpile() function has been removed. It was originally deprecated in the 0.13.0 release. The preferred way to transpile a circuit with a custom PassManager object is to use the run() method of the PassManager object.
The previously deprecated ParametrizedSchedule class has been removed and no longer exists. This class was deprecated as a part of the 0.17.0 release. Instead of using this class you can directly parametrize Schedule or ScheduleBlock objects by specifying a Parameter object to the parametric pulse argument.
The module qiskit.circuit.library.probability_distributions has been removed and no longer exists as per the deprecation notice from qiskit-terra 0.17.0 (released Apr 1, 2021). The affected classes are UniformDistribution, NormalDistribution, and LogNormalDistribution. They are all moved to the qiskit-finance(opens in a new tab) library, into its circuit library module: qiskit_finance.circuit.library.probability_distributions.
The previous qiskit.test.mock.fake_mumbai_v2.FakeMumbaiV2 class has been renamed to FakeMumbaiFractionalCX to differentiate it from the BackendV2 based fake backend for the IBM Mumbai device, qiskit.test.mock.backends.FakeMumbaiV2. If you were previously relying on the FakeMumbaiV2 class to get a fake backend that had fractional applications of CXGate defined in its target you need to use FakeMumbaiFractionalCX class as the FakeMumbaiV2 will no longer have those extra gate definitions in its Target.
The resolver used by QuantumCircuit.append() (and consequently all methods that add an instruction onto a QuantumCircuit) to convert bit specifiers has changed to make it faster and more reliable. Certain constructs like:
```
import numpy as np
from qiskit import QuantumCircuit
 
qc = QuantumCircuit(1, 1)
qc.measure(np.array([0]), np.array([0]))
```
will now work where they previously would incorrectly raise an error, but certain pathological inputs such as:
```
from sympy import E, I, pi
qc.x(E ** (I * pi))
```
will now raise errors where they may have occasionally (erroneously) succeeded before. For almost all correct uses, there should be no noticeable change except for a general speed-up.
The semi-public internal method QuantumCircuit._append() no longer checks the types of its inputs, and assumes that there are no invalid duplicates in its argument lists. This function is used by certain internal parts of Qiskit and other libraries to build up QuantumCircuit instances as quickly as possible by skipping the error checking when the data is already known to be correct. In general, users or functions taking in user data should use the public QuantumCircuit.append() method, which resolves integer bit specifiers, broadcasts its arguments and checks the inputs for correctness.
Cython is no longer a build dependency of Qiskit Terra and is no longer required to be installed when building Qiskit Terra from source.
The preset passmanagers in qiskit.transpiler.preset_passmanagers for all optimization levels 2 and 3 as generated by level_2_pass_manager() and level_3_pass_manager() have been changed to run the VF2Layout by default prior to the layout pass. The VF2Layout pass will quickly check if a perfect layout can be found and supersedes what was previously done for optimization levels 2 and 3 which were using a combination of TrivialLayout and CSPLayout to try and find a perfect layout. This will result in potentially different behavior when transpile() is called by default as it removes a default path for all optimization levels >=2 of using a trivial layout (where circuit.qubits[0] is mapped to physical qubit 0, circuit.qubits[1] is mapped to physical qubit 1, etc) assuming the trivial layout is perfect. If your use case was dependent on the trivial layout you can explictly request it when transpiling by specifying layout_method="trivial" when calling transpile().
The preset pass manager for optimization level 1 (when calling transpile() with optimization_level=1 or when no optimization_level argument is set) as generated by level_1_pass_manager() has been changed so that VF2Layout is called by default to quickly check if a a perfect layout can be found prior to the DenseLayout. However, unlike with optimization level 2 and 3 a trivial layout is still attempted prior to running VF2Layout and if it’s a perfect mapping the output from VF2Layout will be used.

Deprecation Notes

The max_credits argument to execute(), and all of the Qobj configurations (e.g. QasmQobjConfig and PulseQobjConfig), is deprecated and will be removed in a future release. The credit system has not been in use on IBM Quantum backends for two years, and the option has no effect. No alternative is necessary. For example, if you were calling execute() as:
```
job = execute(qc, backend, shots=4321, max_credits=10)
```
you can simply omit the max_credits argument:
```
job = execute(qc, backend, shots=4321)
```
Using an odd integer for the order argument on the constructor of the SuzukiTrotter class is deprecated and will no longer work in a future release. The product formulae used by the SuzukiTrotter are only defined when the order is even as the Suzuki product formulae is symmetric.
The qregs, cregs, layout, and global_phase kwargs to the MatplotlibDrawer, TextDrawing, and QCircuitImage classes, and the calibrations kwarg to the MatplotlibDrawer class, are now deprecated and will be removed in a subsequent release.

Bug Fixes

Fixed an error in the circuit conversion functions circuit_to_gate() and circuit_to_instruction() (and their associated circuit methods QuantumCircuit.to_gate() and QuantumCircuit.to_instruction()) when acting on a circuit with registerless bits, or bits in more than one register.

Fixed an issue where calling QuantumCircuit.copy() on the “body” circuits of a control-flow operation created with the builder interface would raise an error. For example, this was previously an error, but will now return successfully:

from qiskit.circuit import QuantumCircuit, QuantumRegister, ClassicalRegister
 
qreg = QuantumRegister(4)
creg = ClassicalRegister(1)
circ = QuantumCircuit(qreg, creg)
 
with circ.if_test((creg, 0)):
    circ.h(0)
 
if_else_instruction, _, _ = circ.data[0]
true_body = if_else_instruction.params[0]
true_body.copy()

Added a missing entry from the standard session equivalence library between CXGate and CPhaseGate as well as between CXGate and CRZGate.
Fixed an issue where running the == operator between two SparsePauliOp objects would raise an error when the two operators had different numbers of coefficients. For example:
```
op = SparsePauliOp.from_list([("X", 1), ("Y", 1)])
op2 = SparsePauliOp.from_list([("X", 1), ("Y", 1), ("Z", 0)])
print(op == op2)
```
This would previously raise a ValueError instead of returning False.
Fixed support in transpile() for passing a InstructionScheduleMap object to the underlying PassManager based on the Target for BackendV2 based backends. Previously, the transpile() function would not do this processing and any transpiler passes which do not support working with a Target object yet would not have access to the default pulse calibrations for the instructions from a BackendV2 backend.
The AmplitudeAmplifier is now correctly available from the root qiskit.algorithms module directly. Previously it was not included in the re-exported classes off the root module and was only accessible from qiskit.algorithms.amplitude_amplifiers. Fixed #7751(opens in a new tab).
Fixed an issue with the mpl backend for the circuit drawer function circuit_drawer() and the QuantumCircuit.draw() method where gates with conditions would not display properly when a sufficient number of gates caused the drawer to fold over to a second row. Fixed: #7752(opens in a new tab).
Fixed an issue where the HHL.construct_circuit() method under certain conditions would not return a correct QuantumCircuit. Previously, the function had a rounding error in calculating how many qubits were necessary to represent the eigenvalues which would cause an incorrect circuit output.
Fixed an endianness bug in BaseReadoutMitigator.expectation_value() when a string diagonal was passed. It will now correctly be interpreted as little endian in the same manner as the rest of Qiskit Terra, instead of big endian.
Fixed an issue with the quantum_info.partial_trace() when the function was asked to trace out no subsystems, it will now correctly return the DensityMatrix of the input state with all dimensions remaining rather than throwing an error. Fixed #7613(opens in a new tab)
Fixed an issue with the text backend for the circuit drawer function circuit_drawer() and the QuantumCircuit.draw() method when gates that use side text, such as the CPhaseGate and RZZGate gate classes, with classical conditions set would not display properly. Fixed #7532(opens in a new tab).
Fixed an issue with the circuit_drawer() function and draw() method of QuantumCircuit. When using the reverse_bits option with the mpl, latex, or text options, bits without registers did not display in the correct order. Fixed #7303(opens in a new tab).
Fixed an issue in the LocalReadoutMitigator.assignment_matrix() method where it would previously reject an input value for the qubits argument that wasn’t a trivial sequence of qubits in the form: [0, 1, 2, ..., n-1]. This has been corrected so that now any list of qubit indices to be measured are accepted by the method.
Fixed an issue in the StabilizerState.expectation_value() method’s expectation value calculation, where the output expectation value would be incorrect if the input Pauli operator for the oper argument had a non-trivial phase. Fixed #7441(opens in a new tab).
An opflow expression containing the Pauli identity opflow.I no longer produces an IGate when converted to a circuit. This change fixes a difference in expectation; the identity gate in the circuit indicates a delay however in opflow we expect a mathematical identity – meaning no operation at all.
The PauliGate no longer inserts an IGate for Paulis with the label "I".
PauliSumOp equality tests now handle the case when one of the compared items is a single PauliOp. For example, 0 * X + I == I now evaluates to True, whereas it was False prior to this release.
Fixed an issue with the ALAPSchedule and ASAPSchedule transpiler passes when working with instructions that had custom pulse calibrations (i.e. pulse gates) set. Previously, the scheduling passes would not use the duration from the custom pulse calibration for thse instructions which would result in the an incorrect scheduling being generated for the circuit. This has been fixed so that now the scheduling passes will use the duration of the custom pulse calibration for any instruction in the circuit which has a custom calibration.
Fixed support for using ParameterExpression instruction paramaters in the RZXCalibrationBuilder transpiler pass. Previously, if an instruction parameter included a bound ParameterExpression the pass would not be able to handle this correctly.
Stopped the parser in QuantumCircuit.from_qasm_str() and from_qasm_file() from accepting OpenQASM programs that identified themselves as being from a language version other than 2.0. This parser is only for OpenQASM 2.0; support for imported circuits from OpenQASM 3.0 will be added in an upcoming release.
The OpenQASM 3 exporter, qasm3.Exporter, will now escape register and parameter names that clash with reserved OpenQASM 3 keywords by generating a new unique name. Registers and parameters with the same name will no longer have naming clashes in the code output from the OpenQASM 3 exporter. Fixed #7742(opens in a new tab).

Aer 0.10.3

No change

Ignis 0.7.0

No change

IBM Q Provider 0.18.3

No change

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