Qiskit 0.28 release notes

0.28.0

Terra 0.18.0

Prelude

This release includes many new features and bug fixes. The highlights of this release are the introduction of two new transpiler passes, BIPMapping and DynamicalDecoupling, which when combined with the new pulse_optimize kwarg on the UnitarySynthesis pass enables recreating the Quantum Volume 64 results using the techniques described in: https://arxiv.org/abs/2008.08571(opens in a new tab). These new transpiler passes and options and are also generally applicable to optimizing any circuit.

New Features

The measurement_error_mitgation kwarg for the QuantumInstance constructor can now be set to the TensoredMeasFitter class from qiskit-ignis in addition to CompleteMeasFitter that was already supported. If you use TensoredMeasFitter you will also be able to set the new mit_pattern kwarg to specify the qubits on which to use TensoredMeasFitter You can refer to the documentation for mit_pattern in the TensoredMeasFitter documentation for the expected format.
The decomposition methods for single-qubit gates, specified via the basis kwarg, in OneQubitEulerDecomposer has been expanded to now also include the 'ZSXX' basis, for making use of direct $X$ gate as well as $\sqrt{X}$ gate.
Added two new passes AlignMeasures and ValidatePulseGates to the qiskit.transpiler.passes module. These passes are a hardware-aware optimization, and a validation routine that are used to manage alignment restrictions on time allocation of instructions for a backend.

If a backend has a restriction on the alignment of Measure instructions (in terms of quantization in time), the AlignMeasures pass is used to adjust delays in a scheduled circuit to ensure that any Measure instructions in the circuit are aligned given the constraints of the backend. The ValidatePulseGates pass is used to check if any custom pulse gates (gates that have a custom pulse definition in the calibrations attribute of a QuantumCircuit object) are valid given an alignment constraint for the target backend.

In the built-in preset_passmangers used by the transpile() function, these passes get automatically triggered if the alignment constraint, either via the dedicated timing_constraints kwarg on transpile() or has an timing_constraints attribute in the BackendConfiguration object of the backend being targetted.

The backends from IBM Quantum Services (accessible via the qiskit-ibmq-provider(opens in a new tab) package) will provide the alignment information in the near future.

For example:
```
from qiskit import circuit, transpile
from qiskit.test.mock import FakeArmonk
 
backend = FakeArmonk()
 
qc = circuit.QuantumCircuit(1, 1)
qc.x(0)
qc.delay(110, 0, unit="dt")
qc.measure(0, 0)
qc.draw('mpl')
```
```
qct = transpile(qc, backend, scheduling_method='alap',
                timing_constraints={'acquire_alignment': 16})
qct.draw('mpl')
```

A new transpiler pass class qiskit.transpiler.passes.BIPMapping that tries to find the best layout and routing at once by solving a BIP (binary integer programming) problem as described in arXiv:2106.06446(opens in a new tab) has been added.

The BIPMapping pass (named “mapping” to refer to “layout and routing”) represents the mapping problem as a BIP (binary integer programming) problem and relies on CPLEX (cplex) to solve the BIP problem. The dependent libraries including CPLEX can be installed along with qiskit-terra:

pip install qiskit-terra[bip-mapper]

Since the free version of CPLEX can solve only small BIP problems, i.e. mapping of circuits with less than about 5 qubits, the paid version of CPLEX may be needed to map larger circuits.

The BIP mapper scales badly with respect to the number of qubits or gates. For example, it would not work with coupling_map beyond 10 qubits because the BIP solver (CPLEX) could not find any solution within the default time limit.

Note that, if you want to fix physical qubits to be used in the mapping (e.g. running Quantum Volume (QV) circuits), you need to specify coupling_map which contains only the qubits to be used.

Here is a minimal example code to build pass manager to transpile a QV circuit:

num_qubits = 4  # QV16
circ = QuantumVolume(num_qubits=num_qubits)
 
backend = ...
basis_gates = backend.configuration().basis_gates
coupling_map = CouplingMap.from_line(num_qubits)  # supply your own coupling map
 
def _not_mapped(property_set):
    return not property_set["is_swap_mapped"]
 
def _opt_control(property_set):
    return not property_set["depth_fixed_point"]
 
from qiskit.circuit.equivalence_library import SessionEquivalenceLibrary as sel
pm = PassManager()
# preparation
pm.append([
    Unroll3qOrMore(),
    TrivialLayout(coupling_map),
    FullAncillaAllocation(coupling_map),
    EnlargeWithAncilla(),
    BarrierBeforeFinalMeasurements()
])
# mapping
pm.append(BIPMapping(coupling_map))
pm.append(CheckMap(coupling_map))
pm.append(Error(msg="BIP mapper failed to map", action="raise"),
          condition=_not_mapped)
# post optimization
pm.append([
    Depth(),
    FixedPoint("depth"),
    Collect2qBlocks(),
    ConsolidateBlocks(basis_gates=basis_gates),
    UnitarySynthesis(basis_gates),
    Optimize1qGatesDecomposition(basis_gates),
    CommutativeCancellation(),
    UnrollCustomDefinitions(sel, basis_gates),
    BasisTranslator(sel, basis_gates)
], do_while=_opt_control)
 
transpile_circ = pm.run(circ)

A new constructor method initialize_from() was added to the Schedule and ScheduleBlock classes. This method initializes a new empty schedule which takes the attributes from other schedule. For example:
```
sched = Schedule(name='my_sched')
new_sched = Schedule.initialize_from(sched)
 
assert sched.name == new_sched.name
```
A new kwarg, line_discipline, has been added to the job_monitor() function. This kwarg enables changing the carriage return characters used in the job_monitor output. The line_discipline kwarg defaults to '\r', which is what was in use before.
The abstract Pulse class (which is the parent class for classes such as Waveform, Constant, and Gaussian now has a new kwarg on the constructor, limit_amplitude, which can be set to False to disable the previously hard coded amplitude limit of 1. This can also be set as a class attribute directly to change the global default for a Pulse class. For example:
```
from qiskit.pulse.library import Waveform
 
# Change the default value of limit_amplitude to False
Waveform.limit_amplitude = False
wave = Waveform(2.0 * np.exp(1j * 2 * np.pi * np.linspace(0, 1, 1000)))
```
A new class, PauliList, has been added to the qiskit.quantum_info module. This class is used to efficiently represent a list of Pauli operators. This new class inherets from the same parent class as the existing PauliTable (and therefore can be mostly used interchangeably), however it differs from the PauliTable because the qiskit.quantum_info.PauliList class can handle Z4 phases.
Added a new transpiler pass, RemoveBarriers, to qiskit.transpiler.passes. This pass is used to remove all barriers in a circuit.
Add a new optimizer class, SciPyOptimizer, to the qiskit.algorithms.optimizers module. This class is a simple wrapper class of the scipy.optimize.minimize function (documentation(opens in a new tab)) which enables the use of all optimization solvers and all parameters (e.g. callback) which are supported by scipy.optimize.minimize. For example:
```
from qiskit.algorithms.optimizers import SciPyOptimizer
 
values = []
 
def callback(x):
    values.append(x)
 
optimizer = SciPyOptimizer("BFGS", options={"maxiter": 1000}, callback=callback)
```
The HoareOptimizer pass has been improved so that it can now replace a ControlledGate in a circuit with with the base gate if all the control qubits are in the $|1\rangle$ state.
Added two new methods, is_successor() and is_predecessor(), to the DAGCircuit class. These functions are used to check if a node is either a successor or predecessor of another node on the DAGCircuit.
A new transpiler pass, RZXCalibrationBuilderNoEcho, was added to the qiskit.transpiler.passes module. This pass is similar to the existing RZXCalibrationBuilder in that it creates calibrations for an RZXGate(theta), however RZXCalibrationBuilderNoEcho does this without inserting the echo pulses in the pulse schedule. This enables exposing the echo in the cross-resonance sequence as gates so that the transpiler can simplify them. The RZXCalibrationBuilderNoEcho pass only supports the hardware-native direction of the CXGate.

A new kwarg, wrap, has been added to the compose() method of QuantumCircuit. This enables choosing whether composed circuits should be wrapped into an instruction or not. By default this is False, i.e. no wrapping. For example:

from qiskit import QuantumCircuit
circuit = QuantumCircuit(2)
circuit.h([0, 1])
other = QuantumCircuit(2)
other.x([0, 1])
print(circuit.compose(other, wrap=True))  # wrapped
print(circuit.compose(other, wrap=False))  # not wrapped

A new attribute, control_channels, has been added to the PulseBackendConfiguration class. This attribute represents the control channels on a backend as a mapping of qubits to a list of ControlChannel objects.

A new kwarg, epsilon, has been added to the constructor for the Isometry class and the corresponding QuantumCircuit method isometry(). This kwarg enables optionally setting the epsilon tolerance used by an Isometry gate. For example:

import numpy as np
from qiskit import QuantumRegister, QuantumCircuit
 
tolerance = 1e-8
iso = np.eye(2,2)
num_q_output = int(np.log2(iso.shape[0]))
num_q_input = int(np.log2(iso.shape[1]))
q = QuantumRegister(num_q_output)
qc = QuantumCircuit(q)
 
qc.isometry(iso, q[:num_q_input], q[num_q_input:], epsilon=tolerance)

Added a transpiler pass, DynamicalDecoupling, to qiskit.transpiler.passes for inserting dynamical decoupling sequences in idle periods of a circuit (after mapping to physical qubits and scheduling). The pass allows control over the sequence of DD gates, the spacing between them, and the qubits to apply on. For example:

from qiskit.circuit import QuantumCircuit
from qiskit.circuit.library import XGate
from qiskit.transpiler import PassManager, InstructionDurations
from qiskit.transpiler.passes import ALAPSchedule, DynamicalDecoupling
from qiskit.visualization import timeline_drawer
 
circ = QuantumCircuit(4)
circ.h(0)
circ.cx(0, 1)
circ.cx(1, 2)
circ.cx(2, 3)
circ.measure_all()
 
durations = InstructionDurations(
    [("h", 0, 50), ("cx", [0, 1], 700), ("reset", None, 10),
     ("cx", [1, 2], 200), ("cx", [2, 3], 300),
     ("x", None, 50), ("measure", None, 1000)]
)
 
dd_sequence = [XGate(), XGate()]
 
pm = PassManager([ALAPSchedule(durations),
                  DynamicalDecoupling(durations, dd_sequence)])
circ_dd = pm.run(circ)
timeline_drawer(circ_dd)

The QuantumCircuit method qasm() has a new kwarg, encoding, which can be used to optionally set the character encoding of an output QASM file generated by the function. This can be set to any valid codec or alias string from the Python standard library’s codec module(opens in a new tab).
Added a new class, EvolvedOperatorAnsatz, to the qiskit.circuit.library module. This library circuit, which had previously been located in Qiskit Nature(opens in a new tab) , can be used to construct ansatz circuits that consist of time-evolved operators, where the evolution time is a variational parameter. Examples of such ansatz circuits include UCCSD class in the chemistry module of Qiskit Nature or the QAOAAnsatz class.
A new fake backend class is available under qiskit.test.mock for the ibmq_guadalupe backend. As with the other fake backends, this includes a snapshot 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.
A new method children() for the Schedule class has been added. This method is used to return the child schedule components of the Schedule object as a tuple. It returns nested schedules without flattening. This method is equivalent to the private _children() method but has a public and stable interface.
A new optimizer class, GradientDescent, has been added to the qiskit.algorithms.optimizers module. This optimizer class implements a standard gradient descent optimization algorithm for use with quantum variational algorithms, such as VQE. For a detailed description and examples on how to use this class, please refer to the GradientDescent class documentation.
A new optimizer class, QNSPSA, has been added to the qiskit.algorithms.optimizers module. This class implements the Quantum Natural SPSA (QN-SPSA)(opens in a new tab) algorithm, a generalization of the 2-SPSA algorithm, and estimates the Quantum Fisher Information Matrix instead of the Hessian to obtain a stochastic estimate of the Quantum Natural Gradient. For examples on how to use this new optimizer refer to the QNSPSA class documentation.
A new kwarg, second_order, has been added to the constructor of the SPSA class in the qiskit.algorithms.optimizers module. When set to True this enables using second-order SPSA(opens in a new tab). Second order SPSA, or 2-SPSA, is an extension of the ordinary SPSA algorithm that enables estimating the Hessian alongside the gradient, which is used to precondition the gradient before the parameter update step. As a second-order method, this tries to improve convergence of SPSA. For examples on how to use this option refer to the SPSA class documentation.
When using the latex or latex_source output mode of circuit_drawer() or the draw() of QuantumCircuit the style kwarg can now be used just as with the mpl output formatting. However, unlike the mpl output mode only the displaytext field will be used when using the latex or latex_source output modes (because neither supports color).

When using the mpl or latex output methods for the circuit_drawer() function or the draw() of QuantumCircuit, you can now use math mode formatting for text and set color formatting (mpl only) by setting the style kwarg as a dict with a user-generated name or label. For example, to add subscripts and to change a gate color:

from qiskit import QuantumCircuit
from qiskit.circuit.library import HGate
qc = QuantumCircuit(3)
qc.append(HGate(label='h1'), [0])
qc.append(HGate(label='h2'), [1])
qc.append(HGate(label='h3'), [2])
qc.draw('mpl', style={'displaytext': {'h1': 'H_1', 'h2': 'H_2', 'h3': 'H_3'},
    'displaycolor': {'h2': ('#EEDD00', '#FF0000')}})

Added three new classes, CDKMRippleCarryAdder, ClassicalAdder and DraperQFTAdder, to the qiskit.circuit.library module. These new circuit classes are used to perform classical addition of two equally-sized qubit registers. For two registers $|a\rangle_n$ and $|b\rangle_n$ on $n$ qubits, the three new classes perform the operation:

|a\rangle_n |b\rangle_n \mapsto |a\rangle_n |a + b\rangle_{n + 1}.

For example:

from qiskit.circuit import QuantumCircuit
from qiskit.circuit.library import CDKMRippleCarryAdder
from qiskit.quantum_info import Statevector
 
# a encodes |01> = 1
a = QuantumCircuit(2)
a.x(0)
 
# b encodes |10> = 2
b = QuantumCircuit(2)
b.x(1)
 
# adder on 2-bit numbers
adder = CDKMRippleCarryAdder(2)
 
# add the state preparations to the front of the circuit
adder.compose(a, [0, 1], inplace=True, front=True)
adder.compose(b, [2, 3], inplace=True, front=True)
 
# simulate and get the state of all qubits
sv = Statevector(adder)
counts = sv.probabilities_dict()
state = list(counts.keys())[0]  # we only have a single state
 
# skip the input carry (first bit) and the register |a> (last two bits)
result = state[1:-2]
print(result)  # '011' = 3 = 1 + 2

Added two new classes, RGQFTMultiplier and HRSCumulativeMultiplier, to the qiskit.circuit.library module. These classes are used to perform classical multiplication of two equally-sized qubit registers. For two registers $|a\rangle_n$ and $|b\rangle_n$ on $n$ qubits, the two new classes perform the operation

|a\rangle_n |b\rangle_n |0\rangle_{2n} \mapsto |a\rangle_n |b\rangle_n |a \cdot b\rangle_{2n}.

For example:

from qiskit.circuit import QuantumCircuit
from qiskit.circuit.library import RGQFTMultiplier
from qiskit.quantum_info import Statevector
 
num_state_qubits = 2
 
# a encodes |11> = 3
a = QuantumCircuit(num_state_qubits)
a.x(range(num_state_qubits))
 
# b encodes |11> = 3
b = QuantumCircuit(num_state_qubits)
b.x(range(num_state_qubits))
 
# multiplier on 2-bit numbers
multiplier = RGQFTMultiplier(num_state_qubits)
 
# add the state preparations to the front of the circuit
multiplier.compose(a, [0, 1], inplace=True, front=True)
multiplier.compose(b, [2, 3], inplace=True, front=True)
 
# simulate and get the state of all qubits
sv = Statevector(multiplier)
counts = sv.probabilities_dict(decimals=10)
state = list(counts.keys())[0]  # we only have a single state
 
# skip both input registers
result = state[:-2*num_state_qubits]
print(result)  # '1001' = 9 = 3 * 3

The Delay class now can accept a ParameterExpression or Parameter value for the duration kwarg on its constructor and for its duration attribute.

For example:

idle_dur = Parameter('t')
qc = QuantumCircuit(1, 1)
qc.x(0)
qc.delay(idle_dur, 0, 'us')
qc.measure(0, 0)
print(qc)  # parameterized delay in us (micro seconds)
 
# assign before transpilation
assigned = qc.assign_parameters({idle_dur: 0.1})
print(assigned)  # delay in us
transpiled = transpile(assigned, some_backend_with_dt)
print(transpiled)  # delay in dt
 
# assign after transpilation
transpiled = transpile(qc, some_backend_with_dt)
print(transpiled)  # parameterized delay in dt
assigned = transpiled.assign_parameters({idle_dur: 0.1})
print(assigned)  # delay in dt

A new binary serialization format, QPY, has been introduced. It is designed to be a fast binary serialization format that is backwards compatible (QPY files generated with older versions of Qiskit can be loaded by newer versions of Qiskit) that is native to Qiskit. The QPY serialization tooling is available via the qiskit.circuit.qpy_serialization module. For example, to generate a QPY file:

from datetime import datetime
 
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import qpy_serialization
 
qc = QuantumCircuit(
  2, metadata={'created_at': datetime.utcnow().isoformat()}
)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()
 
circuits = [qc] * 5
 
with open('five_bells.qpy', 'wb') as qpy_file:
    qpy_serialization.dump(circuits, qpy_file)

Then the five circuits saved in the QPY file can be loaded with:

from qiskit.circuit.qpy_serialization
 
with open('five_bells.qpy', 'rb') as qpy_file:
    circuits = qpy_serialization.load(qpy_file)

The QPY file format specification is available in the module documentation.

The TwoQubitBasisDecomposer class has been updated to perform pulse optimal decompositions for a basis with CX, √X, and virtual Rz gates as described in https://arxiv.org/pdf/2008.08571(opens in a new tab). Pulse optimal here means that the duration of gates between the CX gates of the decomposition is reduced in exchange for possibly more local gates before or after all the CX gates such that, when composed into a circuit, there is the possibility of single qubit compression with neighboring gates reducing the overall sequence duration.

A new keyword argument, `pulse_optimize, has been added to the constructor for TwoQubitBasisDecomposer to control this:
- None: Attempt pulse optimal decomposition. If a pulse optimal decomposition is unknown for the basis of the decomposer, drop back to the standard decomposition without warning. This is the default setting.
- True: Attempt pulse optimal decomposition. If a pulse optimal decomposition is unknown for the basis of the decomposer, raise QiskitError.
- False: Do not attempt pulse optimal decomposition.
For example:
```
from qiskit.quantum_info import TwoQubitBasisDecomposer
from qiskit.circuit.library import CXGate
from qiskit.quantum_info import random_unitary
 
unitary_matrix = random_unitary(4)
 
decomposer = TwoQubitBasisDecomposer(CXGate(), euler_basis="ZSX", pulse_optimize=True)
circuit = decomposer(unitary_matrix)
```
The transpiler pass UnitarySynthesis located in qiskit.transpiler.passes has been updated to support performing pulse optimal decomposition. This is done primarily with the the pulse_optimize keyword argument which was added to the constructor and used to control whether pulse optimal synthesis is performed. The behavior of this kwarg mirrors the pulse_optimize kwarg in the TwoQubitBasisDecomposer class’s constructor. Additionally, the constructor has another new keyword argument, synth_gates, which is used to specify the list of gate names over which synthesis should be attempted. If None and pulse_optimize is False or None, use "unitary". If None and pulse_optimize is True, use "unitary" and "swap". Since the direction of the CX gate in the synthesis is arbitrary, another keyword argument, natural_direction, is added to consider first a coupling map and then CXGate durations in choosing for which direction of CX to generate the synthesis.
```
from qiskit.circuit import QuantumCircuit
from qiskit.transpiler import PassManager, CouplingMap
from qiskit.transpiler.passes import TrivialLayout, UnitarySynthesis
from qiskit.test.mock import FakeVigo
from qiskit.quantum_info.random import random_unitary
 
backend = FakeVigo()
conf = backend.configuration()
coupling_map = CouplingMap(conf.coupling_map)
triv_layout_pass = TrivialLayout(coupling_map)
circ = QuantumCircuit(2)
circ.unitary(random_unitary(4), [0, 1])
unisynth_pass = UnitarySynthesis(
    basis_gates=conf.basis_gates,
    coupling_map=None,
    backend_props=backend.properties(),
    pulse_optimize=True,
    natural_direction=True,
    synth_gates=['unitary'])
pm = PassManager([triv_layout_pass, unisynth_pass])
optimal_circ = pm.run(circ)
```
A new basis option, 'XZX', was added for the basis argument OneQubitEulerDecomposer class.
Added a new method, get_instructions(), was added to the QuantumCircuit class. This method is used to return all Instruction objects in the circuit which have a name that matches the provided name argument along with its associated qargs and cargs lists of Qubit and Clbit objects.
A new optional extra all has been added to the qiskit-terra package. This enables installing all the optional requirements with a single extra, for example: pip install 'qiskit-terra[all]', Previously, it was necessary to list all the extras individually to install all the optional dependencies simultaneously.
Added two new classes ProbDistribution and QuasiDistribution for dealing with probability distributions and quasiprobability distributions respectively. These objects both are dictionary subclasses that add additional methods for working with probability and quasiprobability distributions.
Added a new settings property to the Optimizer abstract base class that all the optimizer classes in the qiskit.algorithms.optimizers module are based on. This property will return a Python dictionary of the settings for the optimizer that can be used to instantiate another instance of the same optimizer class. For example:
```
from qiskit.algorithms.optimizers import GradientDescent
 
optimizer = GradientDescent(maxiter=10, learning_rate=0.01)
settings = optimizer.settings
new_optimizer = GradientDescent(**settings)
```
The settings dictionary is also potentially useful for serializing optimizer objects using JSON or another serialization format.
A new function, set_config(), has been added to the qiskit.user_config module. This function enables setting values in a user config from the Qiskit API. For example:
```
from qiskit.user_config import set_config
set_config("circuit_drawer", "mpl", section="default", file="settings.conf")
```
which will result in adding a value of circuit_drawer = mpl to the default section in the settings.conf file.

If no file_path argument is specified, the currently used path to the user config file (either the value of the QISKIT_SETTINGS environment variable if set or the default location ~/.qiskit/settings.conf) will be updated. However, changes to the existing config file will not be reflected in the current session since the config file is parsed at import time.
Added a new state class, StabilizerState, to the qiskit.quantum_info module. This class represents a stabilizer simulator state using the convention from Aaronson and Gottesman (2004)(opens in a new tab).
Two new options, 'value' and 'value_desc' were added to the sort kwarg of the qiskit.visualization.plot_histogram() function. When sort is set to either of these options the output visualization will sort the x axis based on the maximum probability for each bitstring. For example:
```
from qiskit.visualization import plot_histogram
 
counts = {
  '000': 5,
  '001': 25,
  '010': 125,
  '011': 625,
  '100': 3125,
  '101': 15625,
  '110': 78125,
  '111': 390625,
}
plot_histogram(counts, sort='value')
```

Known Issues

When running parallel_map() (and functions that internally call parallel_map() such as transpile() and assemble()) on Python 3.9 with QISKIT_PARALLEL set to True in some scenarios it is possible for the program to deadlock and never finish running. To avoid this from happening the default for Python 3.9 was changed to not run in parallel, but if QISKIT_PARALLEL is explicitly enabled then this can still occur.

Upgrade Notes

The minimum version of the retworkx(opens in a new tab) dependency was increased to version 0.9.0. This was done to use new APIs introduced in that release which improved the performance of some transpiler passes.
The default value for QISKIT_PARALLEL on Python 3.9 environments has changed to False, this means that when running on Python 3.9 by default multiprocessing will not be used. This was done to avoid a potential deadlock/hanging issue that can occur when running multiprocessing on Python 3.9 (see the known issues section for more detail). It is still possible to manual enable it by explicitly setting the QISKIT_PARALLEL environment variable to TRUE.
The existing fake backend classes in qiskit.test.mock now strictly implement the BackendV1 interface. This means that if you were manually constructing QasmQobj or PulseQobj object for use with the run() method this will no longer work. The run() method only accepts QuantumCircuit or Schedule objects now. This was necessary to enable testing of new backends implemented without qobj which previously did not have any testing inside qiskit terra. If you need to leverage the fake backends with QasmQobj or PulseQobj new fake legacy backend objects were added to explicitly test the legacy providers interface. This will be removed after the legacy interface is deprecated and removed. Moving forward new fake backends will only implement the BackendV1 interface and will not add new legacy backend classes for new fake backends.
When creating a Pauli object with an invalid string label, a QiskitError is now raised. This is a change from previous releases which would raise an AttributeError on an invalid string label. This change was made to ensure the error message is more informative and distinct from a generic AttributeError.
The output program representation from the pulse builder (qiskit.pulse.builder.build()) has changed from a Schedule to a ScheduleBlock. This new representation disables some timing related operations such as shift and insert. However, this enables parameterized instruction durations within the builder context. For example:
```
from qiskit import pulse
from qiskit.circuit import Parameter
 
dur = Parameter('duration')
 
with pulse.build() as sched:
    with pulse.align_sequential():
        pulse.delay(dur, pulse.DriveChannel(1))
        pulse.play(pulse.Gaussian(dur, 0.1, dur/4), pulse.DriveChannel(0))
 
assigned0 = sched.assign_parameters({dur: 100})
assigned1 = sched.assign_parameters({dur: 200})
```
You can directly pass the duration-assigned schedules to the assembler (or backend), or you can attach them to your quantum circuit as pulse gates.
The tweedledum(opens in a new tab) library which was previously an optional dependency has been made a requirement. This was done because of the wide use of the PhaseOracle (which depends on having tweedledum installed) with several algorithms from qiskit.algorithms.
The optional extra full-featured-simulators which could previously used to install qiskit-aer with something like pip install qiskit-terra[full-featured-simulators] has been removed from the qiskit-terra package. If this was being used to install qiskit-aer with qiskit-terra instead you should rely on the qiskit(opens in a new tab) metapackage or just install qiskit-terra and qiskit-aer together with pip install qiskit-terra qiskit-aer.
A new requirement symengine(opens in a new tab) has been added for Linux (on x86_64, aarch64, and ppc64le) and macOS users (x86_64 and arm64). It is an optional dependency on Windows (and available on PyPi as a precompiled package for 64bit Windows) and other architectures. If it is installed it provides significantly improved performance for the evaluation of Parameter and ParameterExpression objects.

All library circuit classes, i.e. all QuantumCircuit derived classes in qiskit.circuit.library, are now wrapped in a Instruction (or Gate, if they are unitary). For example, importing and drawing the QFT circuit:

before looked like

                                          ┌───┐
q_0: ────────────────────■────────■───────┤ H ├─X─
                   ┌───┐ │        │P(π/2) └───┘ │
q_1: ──────■───────┤ H ├─┼────────■─────────────┼─
     ┌───┐ │P(π/2) └───┘ │P(π/4)                │
q_2: ┤ H ├─■─────────────■──────────────────────X─
     └───┘

and now looks like

     ┌──────┐
q_0: ┤0     ├
     │      │
q_1: ┤1 QFT ├
     │      │
q_2: ┤2     ├
     └──────┘

To obtain the old circuit, you can call the decompose() method on the circuit

This change was primarily made for consistency as before this release some circuit classes in qiskit.circuit.library were previously wrapped in an Instruction or Gate but not all.

Deprecation Notes

The class qiskit.exceptions.QiskitIndexError is deprecated and will be removed in a future release. This exception was not actively being used by anything in Qiskit, if you were using it you can create a custom exception class to replace it.
The kwargs epsilon and factr for the qiskit.algorithms.optimizers.L_BFGS_B constructor and factr kwarg of the P_BFGS optimizer class are deprecated and will be removed in a future release. Instead, please use the eps karg instead of epsilon. The factr kwarg is replaced with ftol. The relationship between the two is ftol = factr * numpy.finfo(float).eps. This change was made to be consistent with the usage of the scipy.optimize.minimize functions 'L-BFGS-B' method. See the: scipy.optimize.minimize(method='L-BFGS-B') documentation(opens in a new tab) for more information on how these new parameters are used.
The legacy providers interface, which consisted of the qiskit.providers.BaseBackend, qiskit.providers.BaseJob, and qiskit.providers.BaseProvider abstract classes, has been deprecated and will be removed in a future release. Instead you should use the versioned interface, which the current abstract class versions are qiskit.providers.BackendV1, qiskit.providers.JobV1, and qiskit.providers.ProviderV1. The V1 objects are mostly backwards compatible to ease migration from the legacy interface to the versioned one. However, expect future versions of the abstract interfaces to diverge more. You can refer to the qiskit.providers documentation for more high level details about the versioned interface.
The condition kwarg to the DAGDepNode constructor along with the corresponding condition attribute of the DAGDepNode have been deprecated and will be removed in a future release. Instead, you can access the condition of a DAGDepNode if the node is of type op, by using DAGDepNode.op.condition.
The condition attribute of the DAGNode class has been deprecated and will be removed in a future release. Instead, you can access the condition of a DAGNode object if the node is of type op, by using DAGNode.op.condition.
The pulse builder (qiskit.pulse.builder.build()) syntax qiskit.pulse.builder.inline() is deprecated and will be removed in a future release. Instead of using this context, you can just remove alignment contexts within the inline context.
The pulse builder (qiskit.pulse.builder.build()) syntax qiskit.pulse.builder.pad() is deprecated and will be removed in a future release. This was done because the ScheduleBlock now being returned by the pulse builder doesn’t support the .insert method (and there is no insert syntax in the builder). The use of timeslot placeholders to block the insertion of other instructions is no longer necessary.

Bug Fixes

The OneQubitEulerDecomposer and TwoQubitBasisDecomposer classes for one and two qubit gate synthesis have been improved to tighten up tolerances, improved repeatability and simplification, and fix several global-phase-tracking bugs.
Fixed an issue in the assignment of the name attribute to Gate generated by multiple calls to the inverse`() method. Prior to this fix when the inverse`() was called it would unconditionally append _dg on each call to inverse. This has been corrected so on a second call of inverse`() the _dg suffix is now removed.
Fixes the triviality check conditions of CZGate, CRZGate, CU1Gate and MCU1Gate in the HoareOptimizer pass. Previously, in some cases the optimizer would remove these gates breaking the semantic equivalence of the transformation.
Fixed an issue when converting a ListOp object of PauliSumOp objects using PauliExpectation or AerPauliExpectation. Previously, it would raise a warning about it converting to a Pauli representation which is potentially expensive. This has been fixed by instead of internally converting the ListOp to a SummedOp of PauliOp objects, it now creates a PauliSumOp which is more efficient. Fixed #6159(opens in a new tab)
Fixed an issue with the NLocal class in the qiskit.circuit.library module where it wouldn’t properly raise an exception at object initialization if an invalid type was used for the reps kwarg which would result in an unexpected runtime error later. A TypeError will now be properly raised if the reps kwarg is not an int value. Fixed #6515(opens in a new tab)
Fixed an issue where the TwoLocal class in the qiskit.circuit.library module did not accept numpy integer types (e.g. numpy.int32, numpy.int64, etc) as a valid input for the entanglement kwarg. Fixed #6455(opens in a new tab)
When loading an OpenQASM2 file or string with the from_qasm_file() or from_qasm_str() constructors for the QuantumCircuit class, if the OpenQASM2 circuit contains an instruction with the name delay this will be mapped to a qiskit.circuit.Delay instruction. For example:
```
from qiskit import QuantumCircuit
 
qasm = """OPENQASM 2.0;
include "qelib1.inc";
opaque delay(time) q;
qreg q[1];
delay(172) q[0];
u3(0.1,0.2,0.3) q[0];
"""
circuit = QuantumCircuit.from_qasm_str(qasm)
circuit.draw()
```
Fixed #6510(opens in a new tab)
Fixed an issue with addition between PauliSumOp objects that had ParameterExpression coefficients. Previously this would result in a QiskitError exception being raised because the addition of the ParameterExpression was not handled correctly. This has been fixed so that addition can be performed between PauliSumOp objects with ParameterExpression coefficients.
Fixed an issue with the initialization of the AmplificationProblem class. The is_good_state kwarg was a required field but incorrectly being treated as optional (and documented as such). This has been fixed and also updated so unless the input oracle is a PhaseOracle object (which provides it’s on evaluation method) the field is required and will raise a TypeError when constructed without is_good_state.
Fixed an issue where adding a control to a ControlledGate with open controls would unset the inner open controls. Fixes #5857(opens in a new tab)
Fixed an issue with the convert() method of the PauliExpectation class where calling it on an operator that was non-Hermitian would return an incorrect result. Fixed #6307(opens in a new tab)
Fixed an issue with the qiskit.pulse.transforms.inline_subroutines() function which would previously incorrectly not remove all the nested components when called on nested schedules. Fixed #6321(opens in a new tab)
Fixed an issue when passing a partially bound callable created with the Python standard library’s functools.partial() function as the schedule kwarg to the add() method of the InstructionScheduleMap class, which would previously result in an error. Fixed #6278(opens in a new tab)
Fixed an issue with the PiecewiseChebyshev when setting the breakpoints to None on an existing object was incorrectly being treated as a breakpoint. This has been corrected so that when it is set to None this will switch back to the default behavior of approximating over the full interval. Fixed #6198(opens in a new tab)
Fixed an issue with the num_connected_components() method of QuantumCircuit which was returning the incorrect number of components when the circuit contains two or more gates conditioned on classical registers. Fixed #6477(opens in a new tab)
Fixed an issue with the qiskit.opflow.expectations module where coefficients of a statefunction were not being multiplied correctly. This also fixed the calculations of Gradients and QFIs when using the PauliExpectation or AerPauliExpectation classes. For example, previously:
```
from qiskit.opflow import StateFn, I, One
 
exp = ~StateFn(I) @ (2 * One)
```
evaluated to 2 for AerPauliExpectation and to 4 for other expectation converters. Since ~StateFn(I) @ (2 * One) is a shorthand notation for ~(2 * One) @ I @ (2 * One), the now correct coefficient of 4 is returned for all expectation converters. Fixed #6497(opens in a new tab)
Fixed the bug that caused to_circuit() to fail when PauliOp had a phase. At the same time, it was made more efficient to use PauliGate.
Fixed an issue where the QASM output generated by the qasm() method of QuantumCircuit for composite gates such as MCXGate and its variants ( MCXGrayCode, MCXRecursive, and MCXVChain) would be incorrect. Now if a Gate in the circuit is not present in qelib1.inc, its definition is added to the output QASM string. Fixed #4943(opens in a new tab) and #3945(opens in a new tab)
Fixed an issue with the circuit_drawer() function and draw() method of QuantumCircuit. When using the mpl or latex output modes, with the cregbundle kwarg set to False and the reverse_bits kwarg set to True, the bits in the classical registers displayed in the same order as when reverse_bits was set to False.
Fixed an issue when using the qiskit.extensions.Initialize instruction which was not correctly setting the global phase of the synthesized definition when constructed. Fixed #5320(opens in a new tab)
Fixed an issue where the bit-order in qiskit.circuit.library.PhaseOracle.evaluate_bitstring() did not agree with the order of the measured bitstring. This fix also affects the execution of the Grover algorithm class if the oracle is specified as a PhaseOracle, which now will now correctly identify the correct bitstring. Fixed #6314(opens in a new tab)
Fixes a bug in Optimize1qGatesDecomposition() previously causing certain short sequences of gates to erroneously not be rewritten.
Fixed an issue in the qiskit.opflow.gradients.Gradient.gradient_wrapper() method with the gradient calculation. Previously, if the operator was not diagonal an incorrect result would be returned in some situations. This has been fixed by using an expectation converter to ensure the result is always correct.
Fixed an issue with the circuit_drawer() function and draw() method of QuantumCircuit with all output modes where it would incorrectly render a custom instruction that includes classical bits in some circumstances. Fixed #3201(opens in a new tab), #3202(opens in a new tab), and #6178(opens in a new tab)
Fixed an issue in circuit_drawer() and the draw() method of the QuantumCircuit class when using the mpl output mode, controlled-Z Gates were incorrectly drawn as asymmetrical. Fixed #5981(opens in a new tab)
Fixed an issue with the OptimizeSwapBeforeMeasure transpiler pass where in some situations a SwapGate that that contained a classical condition would be removed. Fixed #6192(opens in a new tab)
Fixed an issue with the phase of the qiskit.opflow.gradients.QFI class when the qfi_method is set to lin_comb_full which caused the incorrect observable to be evaluated.
Fixed an issue with VQE algorithm class when run with the L_BFGS_B or P_BFGS optimizer classes and gradients are used, the gradient was incorrectly passed as a numpy array instead of the expected list of floats resulting in an error. This has been resolved so you can use gradients with VQE and the L_BFGS_B or P_BFGS optimizers.

Other Notes

The deprecation of the parameters() method for the Instruction class has been reversed. This method was originally deprecated in the 0.17.0, but it is still necessary for several applications, including when running calibration experiments. This method will continue to be supported and will not be removed.

Aer 0.8.2

No change

Ignis 0.6.0

No change

Aqua 0.9.4

No change

IBM Q Provider 0.15.0

New Features

Add support for new method qiskit.providers.ibmq.runtime.RuntimeJob.error_message() which will return a string representing the reason if the job failed.

The inputs parameter to qiskit.providers.ibmq.runtime.IBMRuntimeService.run() method can now be specified as a qiskit.providers.ibmq.runtime.ParameterNamespace instance which supports auto-complete features. You can use qiskit.providers.ibmq.runtime.RuntimeProgram.parameters() to retrieve an ParameterNamespace instance.

For example:

from qiskit import IBMQ
 
provider = IBMQ.load_account()
 
# Set the "sample-program" program parameters.
params = provider.runtime.program(program_id="sample-program").parameters()
params.iterations = 2
 
# Configure backend options
options = {'backend_name': 'ibmq_qasm_simulator'}
 
# Execute the circuit using the "circuit-runner" program.
job = provider.runtime.run(program_id="sample-program",
                           options=options,
                           inputs=params)

The user can now set the visibility (private/public) of a Qiskit Runtime program using qiskit.providers.ibmq.runtime.IBMRuntimeService.set_program_visibility().
An optional boolean parameter pending has been added to qiskit.providers.ibmq.runtime.IBMRuntimeService.jobs() and it allows filtering jobs by their status. If pending is not specified all jobs are returned. If pending is set to True, ‘QUEUED’ and ‘RUNNING’ jobs are returned. If pending is set to False, ‘DONE’, ‘ERROR’ and ‘CANCELLED’ jobs are returned.
Add support for the use_measure_esp flag in the qiskit.providers.ibmq.IBMQBackend.run() method. If True, the backend will use ESP readout for all measurements which are the terminal instruction on that qubit. If used and the backend does not support ESP readout, an error is raised.

Upgrade Notes

qiskit.providers.ibmq.runtime.RuntimeProgram.parameters() is now a method that returns a qiskit.providers.ibmq.runtime.ParameterNamespace instance, which you can use to fill in runtime program parameter values and pass to qiskit.providers.ibmq.runtime.IBMRuntimeService.run().
The open_pulse flag in backend configuration no longer indicates whether a backend supports pulse-level control. As a result, qiskit.providers.ibmq.IBMQBackend.configuration() may return a PulseBackendConfiguration instance even if its open_pulse flag is False.
Job share level is no longer supported due to low adoption and the corresponding interface will be removed in a future release. This means you should no longer pass share_level when creating a job or use qiskit.providers.ibmq.job.IBMQJob.share_level() method to get a job’s share level.

Deprecation Notes

The id instruction has been deprecated on IBM hardware backends. Instead, please use the delay instruction which implements variable-length delays, specified in units of dt. When running a circuit containing an id instruction, a warning will be raised on job submission and any id instructions in the job will be automatically replaced with their equivalent delay instruction.

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