Qiskit 0.23 release notes
0.23.6
Terra 0.16.4
No change
Aer 0.7.5
Prelude
This release is a bugfix release that fixes compatibility in the precompiled binary wheel packages with numpy versions < 1.20.0. The previous release 0.7.4 was building the binaries in a way that would require numpy 1.20.0 which has been resolved now, so the precompiled binary wheel packages will work with any numpy compatible version.
Ignis 0.5.2
No change
Aqua 0.8.2
No change
IBM Q Provider 0.11.1
No change
0.23.5
Terra 0.16.4
Prelude
This release is a bugfix release that primarily fixes compatibility with numpy 1.20.0. This numpy release deprecated their local aliases for Python’s numeric types (np.int
> int
, np.float
> float
, etc.) and the usage of these aliases in Qiskit resulted in a large number of deprecation warnings being emitted. This release fixes this so you can run Qiskit with numpy 1.20.0 without those deprecation warnings.
Aer 0.7.4
Bug Fixes
Fixes compatibility with numpy 1.20.0. This numpy release deprecated their local aliases for Python’s numeric types (np.int
> int
, np.float
> float
, etc.) and the usage of these aliases in Qiskit Aer resulted in a large number of deprecation warnings being emitted. This release fixes this so you can run Qiskit Aer with numpy 1.20.0 without those deprecation warnings.
Ignis 0.5.2
Prelude
This release is a bugfix release that primarily fixes compatibility with numpy 1.20.0. It is also the first release to include support for Python 3.9. Earlier releases (including 0.5.0 and 0.5.1) worked with Python 3.9 but did not indicate this in the package metadata, and there was no upstream testing for those releases. This release fixes that and was tested on Python 3.9 (in addition to 3.6, 3.7, and 3.8).
Bug Fixes
 networkx(opens in a new tab) is explicitly listed as a dependency now. It previously was an implicit dependency as it was required for the
qiskit.ignis.verification.topological_codes
module but was not correctly listed as a depdendency as qiskitterra also requires networkx and is also a depdency of ignis so it would always be installed in practice. However, it is necessary to list it as a requirement for future releases of qiskitterra that will not require networkx. It’s also important to correctly list the dependencies of ignis in case there were a future incompatibility between version requirements.
Aqua 0.8.2
IBM Q Provider 0.11.1
No change
0.23.4
Terra 0.16.3
Bug Fixes
 Fixed an issue introduced in 0.16.2 that would cause errors when running
transpile()
on a circuit with a series of 1 qubit gates and a nongate instruction that only operates on a qubit (e.g.Reset
). Fixes #5736(opens in a new tab)
Aer 0.7.3
No change
Ignis 0.5.1
No change
Aqua 0.8.1
No change
IBM Q Provider 0.11.1
No change
0.23.3
Terra 0.16.2
New Features
 Python 3.9 support has been added in this release. You can now run Qiskit Terra using Python 3.9.
Upgrade Notes
 The class
MCXGrayCode
will now create aC3XGate
ifnum_ctrl_qubits
is 3 and aC4XGate
ifnum_ctrl_qubits
is 4. This is in addition to the previous functionality where for any of the modes of the :class:’qiskit.library.standard_gates.x.MCXGate`, ifnum_ctrl_bits
is 1, aCXGate
is created, and if 2, aCCXGate
is created.
Bug Fixes

Pulse
Delay
instructions are now explicitly assembled asPulseQobjInstruction
objects included in thePulseQobj
output fromassemble()
.Previously, we could ignore
Delay
instructions in aSchedule
as part ofassemble()
as the time was explicit in thePulseQobj
objects. But, now with pulse gates, there are situations where we can schedule ONLY a delay, and not including the delay itself would remove the delay. 
Circuits with custom gate calibrations can now be scheduled with the transpiler without explicitly providing the durations of each circuit calibration.

The
BasisTranslator
andUnroller
passes, in some cases, had not been preserving the global phase of the circuit under transpilation. This has been fixed. 
A bug in
qiskit.pulse.builder.frequency_offset()
where whencompensate_phase
was set a factor of $2\pi$ was missing from the appended phase. 
Fix the global phase of the output of the
QuantumCircuit
methodrepeat()
. If a circuit with global phase is appended to another circuit, the global phase is currently not propagated. Simulators rely on this, since the phase otherwise gets applied multiple times. This sets the global phase ofrepeat()
to 0 before appending the repeated circuit instead of multiplying the existing phase times the number of repetitions. 
Fixes bug in
SparsePauliOp
where multiplying by a certain non Python builtin Numpy scalar types returned incorrect values. Fixes #5408(opens in a new tab) 
The definition of the Hellinger fidelity from has been corrected from the previous defition of $1H(P,Q)$ to $[1H(P,Q)^2]^2$ so that it is equal to the quantum state fidelity of P, Q as diagonal density matrices.

Reduce the number of CX gates in the decomposition of the 3controlled X gate,
C3XGate
. Compiled and optimized in the U CX basis, now only 14 CX and 16 U gates are used instead of 20 and 22, respectively. 
Fixes the issue wherein using Jupyter backend widget or
qiskit.tools.backend_monitor()
would fail if the backend’s basis gates do not include the traditional u1, u2, and u3. 
When running
qiskit.compiler.transpile()
on a list of circuits with a single element, the function used to return a circuit instead of a list. Now, whenqiskit.compiler.transpile()
is called with a list, it will return a list even if that list has a single element. See #5260(opens in a new tab).from qiskit import * qc = QuantumCircuit(2) qc.h(0) qc.cx(0, 1) qc.measure_all() transpiled = transpile([qc]) print(type(transpiled), len(transpiled))
<class 'list'> 1
Aer 0.7.3
New Features
 Python 3.9 support has been added in this release. You can now run Qiskit Aer using Python 3.9 without building from source.
Bug Fixes
 Fixes issue with setting
QasmSimulator
basis gates when using"method"
and"noise_model"
options together, and when using them with a simulator constructed usingfrom_backend()
. Now the listed basis gates will be the intersection of gates supported by the backend configuration, simulation method, and noise model basis gates. If the intersection of the noise model basis gates and simulator basis gates is empty a warning will be logged.  Fixes a bug that resulted in c_if not working when the width of the conditional register was greater than 64. See #1077(opens in a new tab).
 Fixes bug in
from_backend()
andfrom_backend()
wherebasis_gates
was set incorrectly for IBMQ devices with basis gate set['id', 'rz', 'sx', 'x', 'cx']
. Now the noise model will always have the same basis gates as the backend basis gates regardless of whether those instructions have errors in the noise model or not.  Fixes a bug when applying truncation in the matrix product state method of the QasmSimulator.
Ignis 0.5.1
No change
Aqua 0.8.1
No change
IBM Q Provider 0.11.1
No change
0.23.2
Terra 0.16.1
No change
Aer 0.7.2
New Features
 Add the CMake flag
DISABLE_CONAN
(default=``OFF``)s. When installing from source, setting this toON
allows bypassing the Conan package manager to find libraries that are already installed on your system. This is also available as an environment variableDISABLE_CONAN
, which takes precedence over the CMake flag. This is not the official procedure to build AER. Thus, the user is responsible of providing all needed libraries and corresponding files to make them findable to CMake.
Bug Fixes
 Fixes a bug with nested OpenMP flag was being set to true when it shouldn’t be.
Ignis 0.5.1
No change
Aqua 0.8.1
No change
IBM Q Provider 0.11.1
No change
0.23.1
Terra 0.16.1
Bug Fixes
 Fixed an issue where an error was thrown in execute for valid circuits built with delays.
 The QASM definition of ‘c4x’ in qelib1.inc has been corrected to match the standard library definition for C4XGate.
 Fixes a bug in subtraction for quantum channels $A  B$ where $B$ was an
Operator
object. Negation was being applied to the matrix in the Operator representation which is not equivalent to negation in the quantum channel representation.  Changes the way
_evolve_instruction()
access qubits to handle the case of an instruction with multiple registers.
Aer 0.7.1
Upgrade Notes
 The minimum cmake version to build qiskitaer has increased from 3.6 to 3.8. This change was necessary to enable fixing GPU version builds that support running on x86_64 CPUs lacking AVX2 instructions.
Bug Fixes
 qiskitaer with GPU support will now work on systems with x86_64 CPUs lacking AVX2 instructions. Previously, the GPU package would only run if the AVX2 instructions were available. Fixes #1023(opens in a new tab)
 Fixes bug with
AerProvider
where options set on the returned backends usingset_options()
were stored in the provider and would persist for subsequent calls toget_backend()
for the same named backend. Now every call to andbackends()
returns a new instance of the simulator backend that can be configured.  Fixes bug in the error message returned when a circuit contains unsupported simulator instructions. Previously some supported instructions were also being listed in the error message along with the unsupported instructions.
 Fix bug where the “sx”` gate
SXGate
was not listed as a supported gate in the C++ code, in StateOpSet of matrix_product_state.hp.  Fix bug where
"csx"
,"cu2"
,"cu3"
were incorrectly listed as supported basis gates for the"density_matrix"
method of theQasmSimulator
.  In MPS, apply_kraus was operating directly on the input bits in the parameter qubits, instead of on the internal qubits. In the MPS algorithm, the qubits are constantly moving around so all operations should be applied to the internal qubits.
 When invoking MPS::sample_measure, we need to first sort the qubits to the default ordering because this is the assumption in qasm_controller.This is done by invoking the method move_all_qubits_to_sorted_ordering. It was correct in sample_measure_using_apply_measure, but missing in sample_measure_using_probabilities.
Ignis 0.5.1
Bug Fixes
 Fix the
"auto"
method of theTomographyFitter
,StateTomographyFitter
, andProcessTomographyFitter
to only use"cvx"
if CVXPY is installed and a thirdparty SDP solver other than SCS is available. This is because the SCS solver has lower accuracy than other solver methods and often returns a density matrix or Choimatrix that is not completelypositive and fails validation when used with theqiskit.quantum_info.state_fidelity()
orqiskit.quantum_info.process_fidelity()
functions.
Aqua 0.8.1
0.8.1
New Features
 A new algorithm has been added: the Born Openheimer Potential Energy surface for the calculation of potential energy surface along different degrees of freedom of the molecule. The algorithm is called
BOPESSampler
. It further provides functionalities of fitting the potential energy surface to an analytic function of predefined potentials.some details.
Critical Issues
 Be aware that
initial_state
parameter inQAOA
has now different implementation as a result of a bug fix. The previous implementation wrongly mixed the user providedinitial_state
with Hadamard gates. The issue is fixed now. No attention needed if your code does not make use of the user providedinitial_state
parameter.
Bug Fixes
 optimize_svm method of qp_solver would sometimes fail resulting in an error like this ValueError: cannot reshape array of size 1 into shape (200,1) This addresses the issue by adding an L2 norm parameter, lambda2, which defaults to 0.001 but can be changed via the QSVM algorithm, as needed, to facilitate convergence.
 A method
one_letter_symbol
has been removed from theVarType
in the latest build of DOCplex making Aqua incompatible with this version. So instead of using this method an explicit type check of variable types has been introduced in the Aqua optimization module.  :meth`~qiskit.aqua.operators.state_fns.DictStateFn.sample()` could only handle real amplitudes, but it is fixed to handle complex amplitudes. #1311 <https://github.com/Qiskit/qiskitaqua/issues/1311(opens in a new tab)> for more details.
 Trotter class did not use the reps argument in constructor. #1317 <https://github.com/Qiskit/qiskitaqua/issues/1317(opens in a new tab)> for more details.
 Raise an AquaError if :class`qiskit.aqua.operators.converters.CircuitSampler` samples an empty operator. #1321 <https://github.com/Qiskit/qiskitaqua/issues/1321(opens in a new tab)> for more details.
to_opflow()
returns a correct operator when coefficients are complex numbers. #1381 <https://github.com/Qiskit/qiskitaqua/issues/1381(opens in a new tab)> for more details. Let backend simulators validate NoiseModel support instead of restricting to Aer only in QuantumInstance.
 Correctly handle PassManager on QuantumInstance
transpile
method by calling itsrun
method if it exists.  A bug that mixes custom
initial_state
inQAOA
with Hadamard gates has been fixed. This doesn’t change functionality of QAOA if no initial_state is provided by the user. Attention should be taken if your implementation uses QAOA with cusominitial_state
parameter as the optimization results might differ.  Previously, setting seed_simulator=0 in the QuantumInstance did not set any seed. This was only affecting the value 0. This has been fixed.
IBM Q Provider 0.11.1
New Features
qiskit.providers.ibmq.experiment.Experiment
now has three additional attributes, hub, group, and project, that identify the provider used to create the experiment. Methods
qiskit.providers.ibmq.experiment.ExperimentService.experiments()
andqiskit.providers.ibmq.experiment.ExperimentService.analysis_results()
now support alimit
parameter that allows you to limit the number of experiments and analysis results returned.
Upgrade Notes
 A new parameter,
limit
is now the first parameter for bothqiskit.providers.ibmq.experiment.ExperimentService.experiments()
andqiskit.providers.ibmq.experiment.ExperimentService.analysis_results()
methods. Thislimit
has a default value of 10, meaning by deafult only 10 experiments and analysis results will be returned.
Bug Fixes
 Fixes the issue wherein a job could be left in the
CREATING
state if job submit fails halfway through.  Fixes the infinite loop raised when passing an
IBMQRandomService
instance to a child process.
0.23.0
Terra 0.16.0
Prelude
The 0.16.0 release includes several new features and bug fixes. The major features in this release are the following:
 Introduction of scheduled circuits, where delays can be used to control the timing and alignment of operations in the circuit.
 Compilation of quantum circuits from classical functions, such as oracles.
 Ability to compile and optimize single qubit rotations over different Euler basis as well as the phase + squareroot(X) basis (i.e.
['p', 'sx']
), which will replace the older IBM Quantum basis of['u1', 'u2', 'u3']
.  Tracking of
global_phase()
on theQuantumCircuit
class has been extended through thetranspiler
,quantum_info
, andassembler
modules, as well as the BasicAer and Aer simulators. Unitary and state vector simulations will now return global phasecorrect unitary matrices and state vectors.
Also of particular importance for this release is that Python 3.5 is no longer supported. If you are using Qiskit Terra with Python 3.5, the 0.15.2 release is that last version which will work.
New Features

Global R gates have been added to
qiskit.circuit.library
. This includes the global R gate (GR
), global Rx (GRX
) and global Ry (GRY
) gates which are derived from theGR
gate, and global Rz (GRZ
) that is defined in a similar way to theGR
gates. The global R gates are defined on a number of qubits simultaneously, and act as a direct sum of R gates on each qubit.For example:
from qiskit import QuantumCircuit, QuantumRegister import numpy as np num_qubits = 3 qr = QuantumRegister(num_qubits) qc = QuantumCircuit(qr) qc.compose(GR(num_qubits, theta=np.pi/3, phi=2*np.pi/3), inplace=True)
will create a
QuantumCircuit
on aQuantumRegister
of 3 qubits and perform aRGate
of an angle $\theta = \frac{\pi}{3}$ about an axis in the xyplane of the Bloch spheres that makes an angle of $\phi = \frac{2\pi}{3}$ with the xaxis on each qubit. 
A new color scheme,
iqx
, has been added to thempl
backend for the circuit drawerqiskit.visualization.circuit_drawer()
andqiskit.circuit.QuantumCircuit.draw()
. This uses the same color scheme as the Circuit Composer on the IBM Quantum Experience website. There are now 3 available color schemes default
,iqx
, andbw
.There are two ways to select a color scheme. The first is to use a user config file, by default in the
~/.qiskit
directory, in the filesettings.conf
under the[Default]
heading, a user can entercircuit_mpl_style = iqx
to select theiqx
color scheme.The second way is to add
{'name': 'iqx'}
to thestyle
kwarg to theQuantumCircuit.draw
method or to thecircuit_drawer
function. The second way will override the setting in the settings.conf file. For example:from qiskit.circuit import QuantumCircuit circuit = QuantumCircuit(2) circuit.h(0) circuit.cx(0, 1) circuit.measure_all() circuit.draw('mpl', style={'name': 'iqx'})

In the
style
kwarg for the the circuit drawerqiskit.visualization.circuit_drawer()
andqiskit.circuit.QuantumCircuit.draw()
thedisplaycolor
field with thempl
backend now allows for entering both the gate color and the text color for each gate type in the form(gate_color, text_color)
. This allows the use of light and dark gate colors with contrasting text colors. Users can still set only the gate color, in which case thegatetextcolor
field will be used. Gate colors can be set in thestyle
dict for any number of gate types, from one to the entiredisplaycolor
dict. For example:from qiskit.circuit import QuantumCircuit circuit = QuantumCircuit(1) circuit.h(0) style_dict = {'displaycolor': {'h': ('#FA74A6', '#000000')}} circuit.draw('mpl', style=style_dict)
or
style_dict = {'displaycolor': {'h': '#FA74A6'}} circuit.draw('mpl', style=style_dict)

Two alignment contexts are added to the pulse builder (
qiskit.pulse.builder
) to facilitate writing a repeated pulse sequence with delays.qiskit.pulse.builder.align_equispaced()
inserts delays with equivalent length in between pulse schedules within the context.qiskit.pulse.builder.align_func()
offers more advanced control of pulse position. This context takes a callable that calculates a fractional coordinate of ith pulse and aligns pulses within the context. This makes coding of dynamical decoupling easy.

A
rep_delay
parameter has been added to theQasmQobj
class under the run configuration,QasmQobjConfig
. This parameter is used to denote the time between program executions. It must be chosen from the backend range given by theBackendConfiguration
methodrep_delay_range()
. If a value is not provided a backend default,qiskit.providers.models.BackendConfiguration.default_rep_delay
, will be used.rep_delay
will only work on backends which allow for dynamic repetition time. This is can be checked with theBackendConfiguration
propertydynamic_reprate_enabled
. 
The
qobj_schema.json
JSON Schema file inqiskit.schemas
has been updated to include therep_delay
as an optional configuration property for QASM Qobjs. 
The
backend_configuration_schema.json
JSON Schema file inqiskit.schemas
has been updated to includedynamic_reprate_enabled
,rep_delay_range
anddefault_rep_delay
as optional properties for a QASM backend configuration payload. 
A new optimization pass,
qiskit.transpiler.passes.TemplateOptimization
has been added to the transpiler. This pass applies a template matching algorithm described in arXiv:1909.05270(opens in a new tab) that replaces all compatible maximal matches in the circuit.To implement this new transpiler pass a new module,
template_circuits
, was added to the circuit library (qiskit.circuit.library
). This new module contains all the Toffoli circuit templates used in theTemplateOptimization
.This new pass is not currently included in the preset pass managers (
qiskit.transpiler.preset_passmanagers
), to use it you will need to create a customPassManager
. 
A new version of the providers interface has been added. This new interface, which can be found in
qiskit.providers
, provides a new versioning mechanism that will enable changes to the interface to happen in a compatible manner over time. The new interface should be simple to migrate existing providers, as it is mostly identical except for the explicit versioning.Besides having explicitly versioned abstract classes the key changes for the new interface are that the
BackendV1
methodrun()
can now take aQuantumCircuit
orSchedule
object as inputs instead ofQobj
objects. To go along with that options are now part of a backend class so that users can configure run time options when running with a circuit. The final change is thatqiskit.providers.JobV1
can now be synchronous or asynchronous, the exact configuration and method for configuring this is up to the provider, but there are interface hook points to make it explicit which execution model a job is running under in theJobV1
abstract class. 
A new kwarg,
inplace
, has been added to the functionqiskit.result.marginal_counts()
. This kwarg is used to control whether the contents are marginalized in place or a new copy is returned, forResult
object input. This parameter does not have any effect for an inputdict
orCounts
object. 
An initial version of a classical function compiler,
qiskit.circuit.classicalfunction
, has been added. This enables compiling typed python functions (operating only on bits of typeInt1
at the moment) intoQuantumCircuit
objects. For example:from qiskit.circuit import classical_function, Int1 @classical_function def grover_oracle(a: Int1, b: Int1, c: Int1, d: Int1) > Int1: x = not a and b y = d and not c z = not x or y return z quantum_circuit = grover_oracle.synth() quantum_circuit.draw()
The parameter
registerless=False
in theqiskit.circuit.classicalfunction.ClassicalFunction
methodsynth()
creates a circuit with registers refering to the parameter names. For example:quantum_circuit = grover_oracle.synth(registerless=False) quantum_circuit.draw()
A decorated classical function can be used the same way as any other quantum gate when appending it to a circuit.
circuit = QuantumCircuit(5) circuit.append(grover_oracle, range(5)) circuit.draw()
The
GROVER_ORACLE
gate is synthesized when its decomposition is required.circuit.decompose().draw()
The feature requires
tweedledum
, a library for synthesizing quantum circuits, that can be installed via pip withpip install tweedledum
. 
A new class
qiskit.circuit.Delay
for representing a delay instruction in a circuit has been added. A new methoddelay()
is now available for easily appending delays to circuits. This makes it possible to describe timingsensitive experiments (e.g. T1/T2 experiment) in the circuit level.from qiskit import QuantumCircuit qc = QuantumCircuit(1, 1) qc.delay(500, 0, unit='ns') qc.measure(0, 0) qc.draw()

A new argument
scheduling_method
forqiskit.compiler.transpile()
has been added. It is required when transpiling circuits with delays. Ifscheduling_method
is specified, the transpiler returns a scheduled circuit such that all idle times in it are padded with delays (i.e. start time of each instruction is uniquely determined). This makes it possible to see how scheduled instructions (gates) look in the circuit level.from qiskit import QuantumCircuit, transpile from qiskit.test.mock.backends import FakeAthens qc = QuantumCircuit(2) qc.h(0) qc.cx(0, 1) scheduled_circuit = transpile(qc, backend=FakeAthens(), scheduling_method="alap") print("Duration in dt:", scheduled_circuit.duration) scheduled_circuit.draw(idle_wires=False)
See also
timeline_drawer()
for the best visualization of scheduled circuits. 
A new fuction
qiskit.compiler.sequence()
has been also added so that we can convert a scheduled circuit into aSchedule
to make it executable on a pulseenabled backend.from qiskit.compiler import sequence sched = sequence(scheduled_circuit, pulse_enabled_backend)

The
schedule()
has been updated so that it can schedule circuits with delays. Now there are two paths to schedule a circuit with delay:qc = QuantumCircuit(1, 1) qc.h(0) qc.delay(500, 0, unit='ns') qc.h(0) qc.measure(0, 0) sched_path1 = schedule(qc.decompose(), backend) sched_path2 = sequence(transpile(qc, backend, scheduling_method='alap'), backend) assert pad(sched_path1) == sched_path2
Refer to the release notes and documentation for
transpile()
andsequence()
for the details on the other path. 
Added the
GroverOperator
to the circuit library (qiskit.circuit.library
) to construct the Grover operator used in Grover’s search algorithm and Quantum Amplitude Amplification/Estimation. Provided with an oracle in form of a circuit,GroverOperator
creates the textbook Grover operator. To generalize this for amplitude amplification and use a generic operator instead of Hadamard gates as state preparation, thestate_in
argument can be used. 
The
InstructionScheduleMap
methodsget()
andpop()
methods now takeParameterExpression
instances in addition to numerical values for schedule generator parameters. If the generator is a function, expressions may be bound before or within the function call. If the generator is aParametrizedSchedule
, expressions must be bound before the schedule itself is bound/called. 
A new class
$Fx\rangle 0\rangle = \sqrt{1  f(x)}x\rangle 0\rangle + \sqrt{f(x)}x\rangle 1\rangle$LinearAmplitudeFunction
was added to the circuit library (qiskit.circuit.library
) for mapping (piecewise) linear functions on qubit amplitudes,The mapping is based on a controlled Pauli Yrotations and a Taylor approximation, as described in https://arxiv.org/abs/1806.06893(opens in a new tab). This circuit can be used to compute expectation values of linear functions using the quantum amplitude estimation algorithm.

The new jupyter magic
monospaced_output
has been added to theqiskit.tools.jupyter
module. This magic sets the Jupyter notebook output font to “Courier New”, when possible. When used this fonts returns text circuit drawings that are better aligned.import qiskit.tools.jupyter %monospaced_output

A new transpiler pass,
Optimize1qGatesDecomposition
, has been added. This transpiler pass is an alternative to the existingOptimize1qGates
that uses theOneQubitEulerDecomposer
class to decompose and simplify a chain of single qubit gates. This method is compatible with any basis set, whileOptimize1qGates
only works for u1, u2, and u3. The default pass managers foroptimization_level
1, 2, and 3 have been updated to use this new pass if the basis set doesn’t include u1, u2, or u3. 
The
OneQubitEulerDecomposer
now supports two new basis,'PSX'
and'U'
. These can be specified with thebasis
kwarg on the constructor. This will decompose the matrix into a circuit usingPGate
andSXGate
for'PSX'
, andUGate
for'U'
. 
A new method
remove()
has been added to theqiskit.transpiler.PassManager
class. This method enables removing a pass from aPassManager
instance. It works on indexes, similar toreplace()
. For example, to remove theRemoveResetInZeroState
pass from the pass manager used at optimization level 1:from qiskit.transpiler.preset_passmanagers import level_1_pass_manager from qiskit.transpiler.passmanager_config import PassManagerConfig pm = level_1_pass_manager(PassManagerConfig()) pm.draw()
[0] FlowLinear: UnrollCustomDefinitions, BasisTranslator [1] FlowLinear: RemoveResetInZeroState [2] DoWhile: Depth, FixedPoint, Optimize1qGates, CXCancellation
The stage
[1]
withRemoveResetInZeroState
can be removed like this:pass_manager.remove(1) pass_manager.draw()
[0] FlowLinear: UnrollCustomDefinitions, BasisTranslator [1] DoWhile: Depth, FixedPoint, Optimize1qGates, CXCancellation

Several classes to load probability distributions into qubit amplitudes;
UniformDistribution
,NormalDistribution
, andLogNormalDistribution
were added to the circuit library (qiskit.circuit.library
). The normal and lognormal distribution support both univariate and multivariate distributions. These circuits are central to applications in finance where quantum amplitude estimation is used. 
Support for pulse gates has been added to the
QuantumCircuit
class. This enables aQuantumCircuit
to override (for basis gates) or specify (for standard and custom gates) a definition of aGate
operation in terms of timeordered signals across hardware channels. In other words, it enables the option to provide pulselevel custom gate calibrations.The circuits are built exactly as before. For example:
from qiskit import pulse from qiskit.circuit import QuantumCircuit, Gate class RxGate(Gate): def __init__(self, theta): super().__init__('rxtheta', 1, [theta]) circ = QuantumCircuit(1) circ.h(0) circ.append(RxGate(3.14), [0])
Then, the calibration for the gate can be registered using the
QuantumCircuit
methodadd_calibration()
which takes aSchedule
definition as well as the qubits and parameters that it is defined for:# Define the gate implementation as a schedule with pulse.build() as custom_h_schedule: pulse.play(pulse.library.Drag(...), pulse.DriveChannel(0)) with pulse.build() as q1_x180: pulse.play(pulse.library.Gaussian(...), pulse.DriveChannel(1)) # Register the schedule to the gate circ.add_calibration('h', [0], custom_h_schedule) # or gate.name string to register circ.add_calibration(RxGate(3.14), [0], q1_x180) # Can accept gate
Previously, this functionality could only be used through complete Pulse Schedules. Additionally, circuits can now be submitted to backends with your custom definitions (dependent on backend support).
Circuits with pulse gates can still be lowered to a
Schedule
by using theschedule()
function.The calibrated gate can also be transpiled using the regular transpilation process:
transpiled_circuit = transpile(circ, backend)
The transpiled circuit will leave the calibrated gates on the same qubit as the original circuit and will not unroll them to the basis gates.

Support for disassembly of
PulseQobj
objects has been added to theqiskit.assembler.disassemble()
function. For example:from qiskit import pulse from qiskit.assembler.disassemble import disassemble from qiskit.compiler.assemble import assemble from qiskit.test.mock import FakeOpenPulse2Q backend = FakeOpenPulse2Q() d0 = pulse.DriveChannel(0) d1 = pulse.DriveChannel(1) with pulse.build(backend) as sched: with pulse.align_right(): pulse.play(pulse.library.Constant(10, 1.0), d0) pulse.shift_phase(3.11, d0) pulse.measure_all() qobj = assemble(sched, backend=backend, shots=512) scheds, run_config, header = disassemble(qobj)

A new kwarg,
coord_type
has been added toqiskit.visualization.plot_bloch_vector()
. This kwarg enables changing the coordinate system used for the input parameter that describes the positioning of the vector on the Bloch sphere in the generated visualization. There are 2 supported values for this new kwarg,'cartesian'
(the default value) and'spherical'
. If thecoord_type
kwarg is set to'spherical'
the list of parameters taken in are of the form[r, theta, phi]
wherer
is the radius,theta
is the inclination from +z direction, andphi
is the azimuth from +x direction. For example:from numpy import pi from qiskit.visualization import plot_bloch_vector x = 0 y = 0 z = 1 r = 1 theta = pi phi = 0 # Cartesian coordinates, where (x,y,z) are cartesian coordinates # for bloch vector plot_bloch_vector([x,y,z])
plot_bloch_vector([x,y,z], coord_type="cartesian") # Same as line above
# Spherical coordinates, where (r,theta,phi) are spherical coordinates # for bloch vector plot_bloch_vector([r, theta, phi], coord_type="spherical")

Pulse
Schedule
objects now support usingParameterExpression
objects for parameters.For example:
from qiskit.circuit import Parameter from qiskit import pulse alpha = Parameter('⍺') phi = Parameter('ϕ') qubit = Parameter('q') amp = Parameter('amp') schedule = pulse.Schedule() schedule += SetFrequency(alpha, DriveChannel(qubit)) schedule += ShiftPhase(phi, DriveChannel(qubit)) schedule += Play(Gaussian(duration=128, sigma=4, amp=amp), DriveChannel(qubit)) schedule += ShiftPhase(phi, DriveChannel(qubit))
Parameter assignment is done via the
assign_parameters()
method:schedule.assign_parameters({alpha: 4.5e9, phi: 1.57, qubit: 0, amp: 0.2})
Expressions and partial assignment also work, such as:
beta = Parameter('b') schedule += SetFrequency(alpha + beta, DriveChannel(0)) schedule.assign_parameters({alpha: 4.5e9}) schedule.assign_parameters({beta: phi / 6.28})

A new visualization function
timeline_drawer()
was added to theqiskit.visualization
module.For example:
from qiskit.visualization import timeline_drawer from qiskit import QuantumCircuit, transpile from qiskit.test.mock import FakeAthens qc = QuantumCircuit(2) qc.h(0) qc.cx(0,1) timeline_drawer(transpile(qc, FakeAthens(), scheduling_method='alap'))
Upgrade Notes

Type checking for the
params
kwarg of the constructor for theGate
class and its subclasses has been changed. Previously allGate
parameters had to be in a set of allowed types defined in theInstruction
class. Now a new method,validate_parameter()
is used to determine if a parameter type is valid or not. The definition of this method in a subclass will take priority over its parent. For example,UnitaryGate
accepts a parameter of the typenumpy.ndarray
and defines a customvalidate_parameter()
method that returns the parameter if it’s annumpy.ndarray
. This takes priority over the function defined in its parent classGate
. IfUnitaryGate
were to be used as parent for a new class, thisvalidate_parameter
method would be used unless the new child class defines its own method. 
The previously deprecated methods, arguments, and properties named
n_qubits
andnumberofqubits
have been removed. These were deprecated in the 0.13.0 release. The full set of changes are:Class Old New QuantumCircuit
n_qubits
num_qubits
Pauli
numberofqubits
num_qubits
Function Old Argument New Argument qiskit.circuit.random.random_circuit()
n_qubits
num_qubits
qiskit.circuit.library.MSGate
n_qubits
num_qubits

Inserting a parameterized
Gate
instance into aQuantumCircuit
now creates a copy of that gate which is used in the circuit. If changes are made to the instance inserted into the circuit it will no longer be reflected in the gate in the circuit. This change was made to fix an issue when inserting a single parameterizedGate
object into multiple circuits. 
The function
qiskit.result.marginal_counts()
now, by default, does not modify theqiskit.result.Result
instance parameter. Previously, theResult
object was always modified in place. A new kwarginplace
has been addedmarginal_counts()
which enables using the previous behavior wheninplace=True
is set. 
The
U3Gate
definition has been changed to be in terms of theUGate
class. TheUGate
class has no definition. It is therefore not possible to unroll every circuit in terms of U3 and CX anymore. Instead, U and CX can be used for every circuit. 
The deprecated support for running Qiskit Terra with Python 3.5 has been removed. To use Qiskit Terra from this release onward you will now need to use at least Python 3.6. If you are using Python 3.5 the last version which will work is Qiskit Terra 0.15.2.

In the
PulseBackendConfiguration
in thehamiltonian
attributes thevars
field is now returned in a unit of Hz instead of the previously used GHz. This change was made to be consistent with the units used with the other attributes in the class. 
The previously deprecated support for passing in a dictionary as the first positional argument to
DAGNode
constructor has been removed. Using a dictonary for the first positional argument was deprecated in the 0.13.0 release. To create aDAGNode
object now you should directly pass the attributes as kwargs on the constructor. 
The keyword arguments for the circuit gate methods (for example:
qiskit.circuit.QuantumCircuit.cx
)q
,ctl*
, andtgt*
, which were deprecated in the 0.12.0 release, have been removed. Instead, onlyqubit
,control_qubit*
andtarget_qubit*
can be used as named arguments for these methods. 
The previously deprecated module
qiskit.extensions.standard
has been removed. This module has been deprecated since the 0.14.0 release. Theqiskit.circuit.library
can be used instead. Additionally, all the gate classes previously inqiskit.extensions.standard
are still importable fromqiskit.extensions
. 
The previously deprecated gates in the module
qiskit.extensions.quantum_initializer
:DiagGate
, UCG`,UCPauliRotGate
,UCRot
,UCRXGate
,UCX
,UCRYGate
,UCY
,UCRZGate
,UCZ
have been removed. These were all deprecated in the 0.14.0 release and have alternatives available in the circuit library (qiskit.circuit.library
). 
The previously deprecated
qiskit.circuit.QuantumCircuit
gate methodiden()
has been removed. This was deprecated in the 0.13.0 release andi()
orid()
can be used instead.
Deprecation Notes

The use of a
numpy.ndarray
for a parameter in theparams
kwarg for the constructor of theGate
class and subclasses has been deprecated and will be removed in future releases. This was done as part of the refactoring of howparms
type checking is handled for theGate
class. If you have a custom gate class which is a subclass ofGate
directly (or via a different parent in the hierarchy) that accepts anndarray
parameter, you should define a customvalidate_parameter()
method for your class that will return the allowed parameter type. For example:def validate_parameter(self, parameter): """Custom gate parameter has to be an ndarray.""" if isinstance(parameter, numpy.ndarray): return parameter else: raise CircuitError("invalid param type {0} in gate " "{1}".format(type(parameter), self.name))

The
num_ancilla_qubits
property of thePiecewiseLinearPauliRotations
andPolynomialPauliRotations
classes has been deprecated and will be removed in a future release. Instead the propertynum_ancillas
should be used instead. This was done to make it consistent with theQuantumCircuit
methodnum_ancillas()
. 
The
qiskit.circuit.library.MSGate
class has been deprecated, but will remain in place to allow loading of old jobs. It has been replaced with theqiskit.circuit.library.GMS
class which should be used instead. 
The
MSBasisDecomposer
transpiler pass has been deprecated and will be removed in a future release. Theqiskit.transpiler.passes.BasisTranslator
pass can be used instead. 
The
QuantumCircuit
methodsu1
,u2
andu3
are now deprecated. Instead the following replacements can be used.u1(theta) = p(theta) = u(0, 0, theta) u2(phi, lam) = u(pi/2, phi, lam) = p(pi/2 + phi) sx p(pi/2 lam) u3(theta, phi, lam) = u(theta, phi, lam) = p(phi + pi) sx p(theta + pi) sx p(lam)
The gate classes themselves,
U1Gate
,U2Gate
andU3Gate
remain, to allow loading of old jobs.
Bug Fixes
 The
Result
class’s methodsdata()
,get_memory()
,get_counts()
,get_unitary()
, andget_statevector ` will now emit a warning when the ``experiment`()
kwarg is specified for attempting to fetch results using either aQuantumCircuit
orSchedule
instance, when more than one entry matching the instance name is present in theResult
object. Note that only the first entry matching this name will be returned. Fixes #3207(opens in a new tab)  The
qiskit.circuit.QuantumCircuit
methodappend()
can now be used to insert one parameterized gate instance into multiple circuits. This fixes a previous issue where inserting a single parameterizedGate
object into multiple circuits would cause failures when one circuit had a parameter assigned. Fixes #4697(opens in a new tab)  Previously the
qiskit.execute.execute()
function would incorrectly disallow both thebackend
andpass_manager
kwargs to be specified at the same time. This has been fixed so that bothbackend
andpass_manager
can be used together on calls toexecute()
. Fixes #5037(opens in a new tab)  The
QuantumCircuit
methodunitary()
method has been fixed to accept a single integer for theqarg
argument (when adding a 1qubit unitary). The allowed types for theqargs
argument are nowint
,Qubit
, or a list of integers. Fixes #4944(opens in a new tab)  Previously, calling
inverse()
on aBlueprintCircuit
object could fail if its internal data property was not yet populated. This has been fixed so that the callinginverse()
will populate the internal data before generating the inverse of the circuit. Fixes #5140(opens in a new tab)  Fixed an issue when creating a
qiskit.result.Counts
object from an empty data dictionary. Now this will create an emptyCounts
object. Themost_frequent()
method is also updated to raise a more descriptive exception when the object is empty. Fixes #5017(opens in a new tab)  Fixes a bug where setting
ctrl_state
of aUnitaryGate
would be applied twice; once in the creation of the matrix for the controlled unitary and again when calling thedefinition()
method of theqiskit.circuit.ControlledGate
class. This would give the appearence that settingctrl_state
had no effect.  Previously the
ControlledGate
methodinverse()
would not preserve thectrl_state
parameter in some cases. This has been fixed so that callinginverse()
will preserve the valuectrl_state
in its output.  Fixed a bug in the
mpl
output backend of the circuit drawerqiskit.circuit.QuantumCircuit.draw()
andqiskit.visualization.circuit_drawer()
that would cause the drawer to fail if thestyle
kwarg was set to a string. The correct behavior would be to treat that string as a path to a JSON file containing the style sheet for the visualization. This has been fixed, and warnings are raised if the JSON file for the style sheet can’t be loaded.  Fixed an error where loading a QASM file via
from_qasm_file()
orfrom_qasm_str()
would fail if au
,phase(p)
,sx
, orsxdg
gate were present in the QASM file. Fixes #5156(opens in a new tab)  Fixed a bug that would potentially cause registers to be mismapped when unrolling/decomposing a gate defined with only one 2qubit operation.
Aer 0.7.0
Prelude
This 0.7.0 release includes numerous performance improvements and significant enhancements to the simulator interface, and drops support for Python 3.5. The main interface changes are configurable simulator backends, and constructing preconfigured simulators from IBMQ backends. Noise model an basis gate support has also been extended for most of the Qiskit circuit library standard gates, including new support for 1 and 2qubit rotation gates. Performance improvements include adding SIMD support to the density matrix and unitary simulation methods, reducing the used memory and improving the performance of circuits using statevector and density matrix snapshots, and adding support for Kraus instructions to the gate fusion circuit optimization for greatly improving the performance of noisy statevector simulations.
New Features

Adds basis gate support for the
qiskit.circuit.Delay
instruction to theStatevectorSimulator
,UnitarySimulator
, andQasmSimulator
. Note that this gate is treated as an identity gate during simulation and the delay length parameter is ignored. 
Adds basis gate support for the singlequbit gate
qiskit.circuit.library.UGate
to theStatevectorSimulator
,UnitarySimulator
, and the"statevector"
,"density_matrix"
,"matrix_product_state"
, and"extended_stabilizer"
methods of theQasmSimulator
. 
Adds basis gate support for the phase gate
qiskit.circuit.library.PhaseGate
to theStatevectorSimulator
,StatevectorSimulator
,UnitarySimulator
, and the"statevector"
,"density_matrix"
,"matrix_product_state"
, and"extended_stabilizer"
methods of theQasmSimulator
. 
Adds basis gate support for the controlledphase gate
qiskit.circuit.library.CPhaseGate
to theStatevectorSimulator
,StatevectorSimulator
,UnitarySimulator
, and the"statevector"
,"density_matrix"
, and"matrix_product_state"
methods of theQasmSimulator
. 
Adds support for the multicontrolled phase gate
qiskit.circuit.library.MCPhaseGate
to theStatevectorSimulator
,UnitarySimulator
, and the"statevector"
method of theQasmSimulator
. 
Adds support for the $\sqrt(X)$ gate
qiskit.circuit.library.SXGate
to theStatevectorSimulator
,UnitarySimulator
, andQasmSimulator
. 
Adds support for 1 and 2qubit Qiskit circuit library rotation gates
RXGate
,RYGate
,RZGate
,RGate
,RXXGate
,RYYGate
,RZZGate
,RZXGate
to theStatevectorSimulator
,UnitarySimulator
, and the"statevector"
and"density_matrix"
methods of theQasmSimulator
. 
Adds support for multicontrolled rotation gates
"mcr"
,"mcrx"
,"mcry"
,"mcrz"
to theStatevectorSimulator
,UnitarySimulator
, and the"statevector"
method of theQasmSimulator
. 
Make simulator backends configurable. This allows setting persistant options such as simulation method and noise model for each simulator backend object.
The
QasmSimulator
andPulseSimulator
can also be configured from anIBMQBackend
backend object using the :meth:`~qiskit.providers.aer.QasmSimulator.from_backend method. For theQasmSimulator
this will configure the coupling map, basis gates, and basic device noise model based on the backend configuration and properties. For thePulseSimulator
the system model and defaults will be configured automatically from the backend configuration, properties and defaults.For example a noisy density matrix simulator backend can be constructed as
QasmSimulator(method='density_matrix', noise_model=noise_model)
, or an ideal matrix product state simulator asQasmSimulator(method='matrix_product_state')
.A benefit is that a
PulseSimulator
instance configured from a backend better serves as a dropin replacement to the original backend, making it easier to swap in and out a simulator and real backend, e.g. when testing code on a simulator before using a real backend. For example, in the following codeblock, thePulseSimulator
is instantiated from theFakeArmonk()
backend. All configuration and default data is copied into the simulator instance, and so when it is passed as an argument toassemble
, it behaves as if the original backend was supplied (e.g. defaults fromFakeArmonk
will be present and used byassemble
).armonk_sim = qiskit.providers.aer.PulseSimulator.from_backend(FakeArmonk()) pulse_qobj = assemble(schedules, backend=armonk_sim) armonk_sim.run(pulse_qobj)
While the above example is small, the demonstrated ‘dropin replacement’ behavior should greatly improve the usability in more complicated workflows, e.g. when calibration experiments are constructed using backend attributes.

Adds support for qobj global phase to the
StatevectorSimulator
,UnitarySimulator
, and statevector methods of theQasmSimulator
. 
Improves general noisy statevector simulation performance by adding a Kraus method to the gate fusion circuit optimization that allows applying gate fusion to noisy statevector simulations with general Kraus noise.

Use move semantics for statevector and density matrix snapshots for the “statevector” and “density_matrix” methods of the
QasmSimulator
if they are the final instruction in a circuit. This reduces the memory usage of the simulator improves the performance by avoiding copying a large array in the results. 
Adds support for general Kraus
QauntumError
gate errors in theNoiseModel
to the"matrix_product_state"
method of theQasmSimulator
. 
Adds support for density matrix snapshot instruction
qiskit.providers.aer.extensions.SnapshotDensityMatrix
to the"matrix_product_state"
method of theQasmSimulator
. 
Extends the SIMD vectorization of the statevector simulation method to the unitary matrix, superoperator matrix, and density matrix simulation methods. This gives roughtly a 2x performance increase general simulation using the
UnitarySimulator
, the"density_matrix"
method of theQasmSimulator
, gate fusion, and noise simulation. 
Adds a custom vector class to C++ code that has better integration with Pybind11. This haves the memory requirement of the
StatevectorSimulator
by avoiding an memory copy during Python binding of the final simulator state.
Upgrade Notes

AER now uses Lapack to perform some matrix related computations. It uses the Lapack library bundled with OpenBlas (already available in Linux and Macos typical OpenBlas dsitributions; Windows version distributed with AER) or with the accelerate framework in MacOS.

The deprecated support for running qiskitaer with Python 3.5 has been removed. To use qiskitaer >=0.7.0 you will now need at least Python 3.6. If you are using Python 3.5 the last version which will work is qiskitaer 0.6.x.

Updates gate fusion default thresholds so that gate fusion will be applied to circuits with of more than 14 qubits for statevector simulations on the
StatevectorSimulator
andQasmSimulator
.For the
"density_matrix"
method of theQasmSimulator
and for theUnitarySimulator
gate fusion will be applied to circuits with more than 7 qubits.Custom qubit threshold values can be set using the
fusion_threshold
backend option iebackend.set_options(fusion_threshold=10)

Changes
fusion_threshold
backend option to apply fusion when the number of qubits is above the threshold, not equal or above the threshold, to match the behavior of the OpenMP qubit threshold parameter.
Deprecation Notes
qiskit.providers.aer.noise.NoiseModel.set_x90_single_qubit_gates()
has been deprecated as unrolling to custom basis gates has been added to the qiskit transpiler. The correct way to use an X90 based noise model is to define noise on the Sqrt(X)"sx"
or"rx"
gate and one of the singlequbit phase gates"u1"
,"rx"
, or"p"
in the noise model. The
variance
kwarg of Snapshot instructions has been deprecated. This function computed the sample variance in the snapshot due to noise model sampling, not the variance due to measurement statistics so was often being used incorrectly. If noise modeling variance is required single shot snapshots should be used so variance can be computed manually in postprocessing.
Bug Fixes
 Fixes bug in the
StatevectorSimulator
that caused it to always run as CPU with doubleprecision without SIMD/AVX2 support even on systems with AVX2, or when singleprecision or the GPU method was specified in the backend options.  Fixes some forloops in C++ code that were iterating over copies rather than references of container elements.
 Fixes a bug where snapshot data was always copied from C++ to Python rather than moved where possible. This will halve memory usage and improve simulation time when using large statevector or density matrix snapshots.
 Fix State::snapshot_pauli_expval to return correct Y expectation value in stabilizer simulator. Refer to #895 <https://github.com/Qiskit/qiskitaer/issues/895(opens in a new tab)> for more details.
 The controller_execute wrappers have been adjusted to be functors (objects) rather than free functions. Among other things, this allows them to be used in multiprocessing.pool.map calls.
 Add missing available memory checks for the
StatevectorSimulator
andUnitarySimulator
. This throws an exception if the memory required to simulate the number of qubits in a circuit exceeds the available memory of the system.
Ignis 0.5.0
Prelude
This release includes a new module for expectation value measurement error mitigation, improved plotting functionality for quantum volume experiments, several bug fixes, and drops support for Python 3.5.
New Features

The
qiskit.ignis.verification.randomized_benchmarking.randomized_benchmarking_seq()
function allows an optional input of gate objects as interleaved_elem. In addition, the CNOTDihedral classqiskit.ignis.verification.randomized_benchmarking.CNOTDihedral
has a new method to_instruction, and the existing from_circuit method has an optional input of an Instruction (in addition to QuantumCircuit). 
The
qiskit.ignis.verification.randomized_benchmarking.CNOTDihedral
now contains the following new features. Initialization from various types of objects: CNOTDihedral, ScalarOp, QuantumCircuit, Instruction and Pauli. Converting to a matrix using to_matrix and to an operator using to_operator. Tensor product methods tensor and expand. Calculation of the adjoint, conjugate and transpose using conjugate, adjoint and transpose methods. Verify that an element is CNOTDihedral using is_cnotdihedral method. Decomposition method to_circuit of a CNOTDihedral element into a circuit was extended to allow any number of qubits, based on the function decompose_cnotdihedral_general. 
Adds expectation value measurement error mitigation to the mitigation module. This supports using complete Nqubit assignment matrix, singlequbit tensored assignment matrix, or continuous time Markov process (CTMP) [1] measurement error mitigation when computing expectation values of diagonal operators from counts dictionaries. Expectation values are computed using the using the
qiskit.ignis.mitigation.expectation_value()
function.Calibration circuits for calibrating a measurement error mitigator are generated using the
qiskit.ignis.mitigation.expval_meas_mitigator_circuits()
function, and the result fitted using theqiskit.ignis.mitigation.ExpvalMeasMitigatorFitter
class. The fitter returns a mitigator object can the be supplied as an argument to theexpectation_value()
function to apply mitigation.[1] S Bravyi, S Sheldon, A Kandala, DC Mckay, JM Gambetta,
Mitigating measurement errors in multiqubit experiments, arXiv:2006.14044 [quantph].
Example:
The following example shows calibrating a 5qubit expectation value measurement error mitigator using the
'tensored'
method.from qiskit import execute from qiskit.test.mock import FakeVigo import qiskit.ignis.mitigation as mit backend = FakeVigo() num_qubits = backend.configuration().num_qubits # Generate calibration circuits circuits, metadata = mit.expval_meas_mitigator_circuits( num_qubits, method='tensored') result = execute(circuits, backend, shots=8192).result() # Fit mitigator mitigator = mit.ExpvalMeasMitigatorFitter(result, metadata).fit() # Plot fitted Nqubit assignment matrix mitigator.plot_assignment_matrix()
The following shows how to use the above mitigator to apply measurement error mitigation to expectation value computations
from qiskit import QuantumCircuit # Test Circuit with expectation value 1. qc = QuantumCircuit(num_qubits) qc.x(range(num_qubits)) qc.measure_all() # Execute shots = 8192 seed_simulator = 1999 result = execute(qc, backend, shots=8192, seed_simulator=1999).result() counts = result.get_counts(0) # Expectation value of Z^N without mitigation expval_nomit, error_nomit = mit.expectation_value(counts) print('Expval (no mitigation): {:.2f} \u00B1 {:.2f}'.format( expval_nomit, error_nomit)) # Expectation value of Z^N with mitigation expval_mit, error_mit = mit.expectation_value(counts, meas_mitigator=mitigator) print('Expval (with mitigation): {:.2f} \u00B1 {:.2f}'.format( expval_mit, error_mit))

Adds Numba as an optional dependency. Numba is used to significantly increase the performance of the
qiskit.ignis.mitigation.CTMPExpvalMeasMitigator
class used for expectation value measurement error mitigation with the CTMP method. 
Add two methods to
qiskit.ignis.verification.quantum_volume.QVFitter
.qiskit.ignis.verification.quantum_volume.QVFitter.calc_z_value()
to calculate z value in standard normal distribution using mean and standard deviation sigma. If sigma = 0, it raises a warning and assigns a small value (1e10) for sigma so that the code still runs.qiskit.ignis.verification.quantum_volume.QVFitter.calc_confidence_level()
to calculate confidence level using z value.

Store confidence level even when hmean < 2/3 in
qiskit.ignis.verification.quantum_volume.QVFitter.qv_success()
. 
Add explanations for how to calculate statistics based on binomial distribution in
qiskit.ignis.verification.quantum_volume.QVFitter.calc_statistics()
. 
The
qiskit.ignis.verification.QVFitter
methodplot_qv_data()
has been updated to return amatplotlib.Figure
object. Previously, it would not return anything. By returning a figure this makes it easier to integrate the visualizations into a largermatplotlib
workflow. 
The error bars in the figure produced by the
qiskit.ignis.verification.QVFitter
methodqiskit.ignis.verification.QVFitter.plot_qv_data()
has been updated to represent twosigma confidence intervals. Previously, the error bars represent onesigma confidence intervals. The success criteria of Quantum Volume benchmarking requires heavy output probability > 2/3 with onesided twosigma confidence (~97.7%). Changing error bars to represent twosigma confidence intervals allows easily identification of success in the figure. 
A new kwarg,
figsize
has been added to theqiskit.ignis.verification.QVFitter
methodqiskit.ignis.verification.QVFitter.plot_qv_data()
. This kwarg takes in a tuple of the form(x, y)
wherex
andy
are the dimension in inches to make the generated plot. 
The
qiskit.ignis.verification.quantum_volume.QVFitter.plot_hop_accumulative()
method has been added to plot heavy output probability (HOP) vs number of trials similar to Figure 2a of Quantum Volume 64 paper (arXiv:2008.08571(opens in a new tab)). HOP of individual trials are plotted as scatters and cummulative HOP are plotted in red line. Twosigma confidence intervals are plotted as shaded area and 2/3 success threshold is plotted as dashed line. 
The
qiskit.ignis.verification.quantum_volume.QVFitter.plot_qv_trial()
method has been added to plot individual trials, leveraging on theqiskit.visualization.plot_histogram()
method from Qiskit Terra. Bitstring counts are plotted as overlapping histograms for ideal (hollow) and experimental (filled) values. Experimental heavy output probability are shown on the legend. Median probability is plotted as red dashed line.
Upgrade Notes
 The deprecated support for running qiskitignis with Python 3.5 has been removed. To use qiskitignis >=0.5.0 you will now need at least Python 3.6. If you are using Python 3.5 the last version which will work is qiskitignis 0.4.x.
Bug Fixes
 Fixing a bug in the class
qiskit.ignis.verification.randomized_benchmarking.CNOTDihedral
for elements with more than 5 quits.  Fix the confidence level threshold for
qiskit.ignis.verification.quantum_volume.QVFitter.qv_success()
to 0.977 corresponding to z = 2 as defined by the QV paper Algorithm 1.  Fix a bug at
qiskit.ignis.verification.randomized_benchmarking.randomized_benchmarking_seq()
which caused all the subsystems with the same size in the given rb_pattern to have the same gates when a ‘rand_seed’ parameter was given to the function.
Aqua 0.8.0
Prelude
This release introduces an interface for running the available methods for Bosonic problems. In particular we introduced a full interface for running vibronic structure calculations.
This release introduces an interface for excited states calculations. It is now easier for the user to create a general excited states calculation. This calculation is based on a Driver which provides the relevant information about the molecule, a Transformation which provides the information about the mapping of the problem into a qubit Hamiltonian, and finally a Solver. The Solver is the specific way which the excited states calculation is done (the algorithm). This structure follows the one of the ground state calculations. The results are modified to take lists of expectation values instead of a single one. The QEOM and NumpyEigensolver are adapted to the new structure. A factory is introduced to run a numpy eigensolver with a specific filter (to target states of specific symmetries).
VQE expectation computation with Aer qasm_simulator now defaults to a computation that has the expected shot noise behavior.
New Features

Introduced an option warm_start that should be used when tuning other options does not help. When this option is enabled, a relaxed problem (all variables are continuous) is solved first and the solution is used to initialize the state of the optimizer before it starts the iterative process in the solve method.

The amplitude estimation algorithms now use
QuantumCircuit
objects as inputs to specify the A and Q operators. This change goes along with the introduction of theGroverOperator
in the circuit library, which allows an intuitive and fast construction of different Q operators. For example, a Bernoulliexperiment can now be constructed asimport numpy as np from qiskit import QuantumCircuit from qiskit.aqua.algorithms import AmplitudeEstimation probability = 0.5 angle = 2 * np.sqrt(np.arcsin(probability)) a_operator = QuantumCircuit(1) a_operator.ry(angle, 0) # construct directly q_operator = QuantumCircuit(1) q_operator.ry(2 * angle, 0) # construct via Grover operator from qiskit.circuit.library import GroverOperator oracle = QuantumCircuit(1) oracle.z(0) # good state = the qubit is in state 1> q_operator = GroverOperator(oracle, state_preparation=a_operator) # use default construction in QAE q_operator = None ae = AmplitudeEstimation(a_operator, q_operator)

Add the possibility to compute Conditional Value at Risk (CVaR) expectation values.
Given a diagonal observable H, often corresponding to the objective function of an optimization problem, we are often not as interested in minimizing the average energy of our observed measurements. In this context, we are satisfied if at least some of our measurements achieve low energy. (Note that this is emphatically not the case for chemistry problems).
To this end, one might consider using the best observed sample as a cost function during variational optimization. The issue here, is that this can result in a nonsmooth optimization surface. To resolve this issue, we can smooth the optimization surface by using not just the best observed sample, but instead average over some fraction of best observed samples. This is exactly what the CVaR estimator accomplishes [1].
Let $\alpha$ be a real number in $[0,1]$ which specifies the fraction of best observed samples which are used to compute the objective function. Observe that if $\alpha = 1$, CVaR is equivalent to a standard expectation value. Similarly, if $\alpha = 0$, then CVaR corresponds to using the best observed sample. Intermediate values of $\alpha$ interpolate between these two objective functions.
The functionality to use CVaR is included into the operator flow through a new subclass of OperatorStateFn called CVaRMeasurement. This new StateFn object is instantied in the same way as an OperatorMeasurement with the exception that it also accepts an alpha parameter and that it automatically enforces the is_measurement attribute to be True. Observe that it is unclear what a CVaRStateFn would represent were it not a measurement.
Examples:
qc = QuantumCircuit(1) qc.h(0) op = CVaRMeasurement(Z, alpha=0.5) @ CircuitStateFn(primitive=qc, coeff=1.0) result = op.eval()
Similarly, an operator corresponding to a standard expectation value can be converted into a CVaR expectation using the CVaRExpectation converter.
Examples:
qc = QuantumCircuit(1) qc.h(0) op = ~StateFn(Z) @ CircuitStateFn(primitive=qc, coeff=1.0) cvar_expecation = CVaRExpectation(alpha=0.1).convert(op) result = cvar_expecation.eval()
See [1] for additional details regarding this technique and it’s empircal performance.
References:
[1]: Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., and Woerner, S.,
“Improving Variational Quantum Optimization using CVaR” arXiv:1907.04769(opens in a new tab)

New interface
Eigensolver
for Eigensolver algorithms. 
An interface for excited states calculation has been added to the chemistry module. It is now easier for the user to create a general excited states calculation. This calculation is based on a
Driver
which provides the relevant information about the molecule, aTransformation
which provides the information about the mapping of the problem into a qubit Hamiltonian, and finally a Solver. The Solver is the specific way which the excited states calculation is done (the algorithm). This structure follows the one of the ground state calculations. The results are modified to take lists of expectation values instead of a single one. TheQEOM
andNumpyEigensolver
are adapted to the new structure. A factory is introduced to run a numpy eigensolver with a specific filter (to target states of specific symmetries). 
In addition to the workflows for solving Fermionic problems, interfaces for calculating Bosonic ground and excited states have been added. In particular we introduced a full interface for running vibronic structure calculations.

The
OrbitalOptimizationVQE
has been added as new ground state solver in the chemistry module. This solver allows for the simulatneous optimization of the variational parameters and the orbitals of the molecule. The algorithm is introduced in Sokolov et al., The Journal of Chemical Physics 152 (12). 
A new algorithm has been added: the Born Openheimer Potential Energy surface for the calculation of potential energy surface along different degrees of freedom of the molecule. The algorithm is called
BOPESSampler
. It further provides functionalities of fitting the potential energy surface to an analytic function of predefined potentials. 
A feasibility check of the obtained solution has been added to all optimizers in the optimization stack. This has been implemented by adding two new methods to
QuadraticProgram
: *get_feasibility_info(self, x: Union[List[float], np.ndarray])
accepts an array and returns whether this solution is feasible and a list of violated variables(violated bounds) and a list of violated constraints. *is_feasible(self, x: Union[List[float], np.ndarray])
accepts an array and returns whether this solution is feasible or not. 
Add circuitbased versions of
FixedIncomeExpectedValue
,EuropeanCallDelta
,GaussianConditionalIndependenceModel
andEuropeanCallExpectedValue
toqiskit.finance.applications
. 
Gradient Framework.
qiskit.operators.gradients
Given an operator that represents either a quantum state resp. an expectation value, the gradient framework enables the evaluation of gradients, natural gradients, Hessians, as well as the Quantum Fisher Information.Suppose a parameterized quantum state ψ(θ)〉 = V(θ)ψ〉 with input state ψ〉 and parametrized Ansatz V(θ), and an Operator O(ω).
Gradients: We want to compute $d⟨ψ(θ)O(ω)ψ(θ)〉/ dω$ resp. $d⟨ψ(θ)O(ω)ψ(θ)〉/ dθ$ resp. $d⟨ψ(θ)i〉⟨iψ(θ)〉/ dθ$.
The last case corresponds to the gradient w.r.t. the sampling probabilities of ψ(θ). These gradients can be computed with different methods, i.e. a parameter shift, a linear combination of unitaries and a finite difference method.
Examples:
x = Parameter('x') ham = x * X a = Parameter('a') q = QuantumRegister(1) qc = QuantumCircuit(q) qc.h(q) qc.p(params[0], q[0]) op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.) value_dict = {x: 0.1, a: np.pi / 4} ham_grad = Gradient(grad_method='param_shift').convert(operator=op, params=[x]) ham_grad.assign_parameters(value_dict).eval() state_grad = Gradient(grad_method='lin_comb').convert(operator=op, params=[a]) state_grad.assign_parameters(value_dict).eval() prob_grad = Gradient(grad_method='fin_diff').convert(operator=CircuitStateFn(primitive=qc, coeff=1.), params=[a]) prob_grad.assign_parameters(value_dict).eval()
Hessians: We want to compute $d^2⟨ψ(θ)O(ω)ψ(θ)〉/ dω^2$ resp. $d^2⟨ψ(θ)O(ω)ψ(θ)〉/ dθ^2$ resp. $d^2⟨ψ(θ)O(ω)ψ(θ)〉/ dθdω$ resp. $d^2⟨ψ(θ)i〉⟨iψ(θ)〉/ dθ^2$.
The last case corresponds to the Hessian w.r.t. the sampling probabilities of ψ(θ). Just as the first order gradients, the Hessians can be evaluated with different methods, i.e. a parameter shift, a linear combination of unitaries and a finite difference method. Given a tuple of parameters
Hessian().convert(op, param_tuple)
returns the value for the second order derivative. If a list of parameters is givenHessian().convert(op, param_list)
returns the full Hessian for all the given parameters according to the given parameter order.QFI: The Quantum Fisher Information QFI is a metric tensor which is representative for the representation capacity of a parameterized quantum state ψ(θ)〉 = V(θ)ψ〉 generated by an input state ψ〉 and a parametrized Ansatz V(θ). The entries of the QFI for a pure state read $[QFI]kl= Re[〈∂kψ∂lψ〉−〈∂kψψ〉〈ψ∂lψ〉] * 4$.
Just as for the previous derivative types, the QFI can be computed using different methods: a full representation based on a linear combination of unitaries implementation, a blockdiagonal and a diagonal representation based on an overlap method.
Examples:
q = QuantumRegister(1) qc = QuantumCircuit(q) qc.h(q) qc.p(params[0], q[0]) op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.) value_dict = {x: 0.1, a: np.pi / 4} qfi = QFI('lin_comb_full').convert(operator=CircuitStateFn(primitive=qc, coeff=1.), params=[a]) qfi.assign_parameters(value_dict).eval()
The combination of the QFI and the gradient lead to a special form of a gradient, namely
NaturalGradients: The natural gradient is a special gradient method which rescales a gradient w.r.t. a state parameter with the inverse of the corresponding Quantum Fisher Information (QFI) $QFI^1 d⟨ψ(θ)O(ω)ψ(θ)〉/ dθ$. Hereby, we can choose a gradient as well as a QFI method and a regularization method which is used together with a least square solver instead of exact invertion of the QFI:
Examples:
op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.) nat_grad = NaturalGradient(grad_method='lin_comb, qfi_method='lin_comb_full', \ regularization='ridge').convert(operator=op, params=params)
The gradient framework is also compatible with the optimizers from qiskit.aqua.components.optimizers. The derivative classes come with a gradient_wrapper() function which returns the corresponding callable.

Introduces
transformations
for the fermionic and bosonic transformation of a problem instance. Transforms the fermionic operator to qubit operator. Respective class for the transformation isfermionic_transformation
Introduces in algorithmsground_state_solvers
for the calculation of ground state properties. The calculation can be done either using anMinimumEigensolver
or usingAdaptVQE
Introduceschemistry/results
where the eigenstate_result and the electronic_structure_result are also used for the algorithms. Introduces Minimum Eigensolver factoriesminimum_eigensolver_factories
where chemistry specific minimum eigensolvers can be initialized Introduces orbital optimization vqeoovqe
as a ground state solver for chemistry applications 
New Algorithm result classes:
Grover
method_run()
returns classGroverResult
.AmplitudeEstimation
method_run()
returns classAmplitudeEstimationResult
.IterativeAmplitudeEstimation
method_run()
returns classIterativeAmplitudeEstimationResult
.MaximumLikelihoodAmplitudeEstimation
method_run()
returns classMaximumLikelihoodAmplitudeEstimationResult
.All new result classes are backwards compatible with previous result dictionary.

New Linear Solver result classes:
HHL
method_run()
returns classHHLResult
.NumPyLSsolver
method_run()
returns classNumPyLSsolverResult
.All new result classes are backwards compatible with previous result dictionary.

MinimumEigenOptimizationResult
now exposes properties:samples
andeigensolver_result
. The latter is obtained from the underlying algorithm used by the optimizer and specific to the algorithm.RecursiveMinimumEigenOptimizer
now returns an instance of the result classRecursiveMinimumEigenOptimizationResult
which in turn may contains intermediate results obtained from the underlying algorithms. The dedicated result class exposes propertiesreplacements
andhistory
that are specific to this optimizer. The depth of the history is managed by thehistory
parameter of the optimizer. 
GroverOptimizer
now returns an instance ofGroverOptimizationResult
and this result class exposes propertiesoperation_counts
,n_input_qubits
, andn_output_qubits
directly. These properties are not available in theraw_results
dictionary anymore. 
SlsqpOptimizer
now returns an instance ofSlsqpOptimizationResult
and this result class exposes additional properties specific to the SLSQP implementation. 
Support passing
QuantumCircuit
objects as generator circuits into theQuantumGenerator
. 
Removes the restriction to real input vectors in CircuitStateFn.from_vector. The method calls extensions.Initialize. The latter explicitly supports (in API and documentation) complex input vectors. So this restriction seems unnecessary.

Simplified AbelianGrouper using a graph coloring algorithm of retworkx. It is faster than the numpybased coloring algorithm.

Allow calling
eval
on state function objects with no argument, which returns theVectorStateFn
representation of the state function. This is consistent behavior withOperatorBase.eval
, which returns theMatrixOp
representation, if no argument is passed. 
Adds
max_iterations
to theVQEAdapt
class in order to allow limiting the maximum number of iterations performed by the algorithm. 
VQE expectation computation with Aer qasm_simulator now defaults to a computation that has the expected shot noise behavior. The special Aer snapshot based computation, that is much faster, with the ideal output similar to state vector simulator, may still be chosen but like before Aqua 0.7 it now no longer defaults to this but can be chosen.
Upgrade Notes
 Extension of the previous Analytic Quantum Gradient Descent (AQGD) classical optimizer with the AQGD with Epochs. Now AQGD performs the gradient descent optimization with a momentum term, analytic gradients, and an added customized step length schedule for parametrized quantum gates. Gradients are computed “analytically” using the quantum circuit when evaluating the objective function.
 The deprecated support for running qiskitaqua with Python 3.5 has been removed. To use qiskitaqua >=0.8.0 you will now need at least Python 3.6. If you are using Python 3.5 the last version which will work is qiskitaqua 0.7.x.
 Added retworkx as a new dependency.
Deprecation Notes

The
i_objective
argument of the amplitude estimation algorithms has been renamed toobjective_qubits
. 
TransformationType

QubitMappingType

Deprecate the
CircuitFactory
and derived types. TheCircuitFactory
has been introduced as temporary class when theQuantumCircuit
missed some features necessary for applications in Aqua. Now that the circuit has all required functionality, the circuit factory can be removed. The replacements are shown in the following table.Circuit factory class  Replacement + CircuitFactory  use QuantumCircuit  UncertaintyModel   UnivariateDistribution   MultivariateDistribution   NormalDistribution  qiskit.circuit.library.NormalDistribution MultivariateNormalDistribution  qiskit.circuit.library.NormalDistribution LogNormalDistribution  qiskit.circuit.library.LogNormalDistribution MultivariateLogNormalDistribution  qiskit.circuit.library.LogNormalDistribution UniformDistribution  qiskit.circuit.library.UniformDistribution MultivariateUniformDistribution  qiskit.circuit.library.UniformDistribution UnivariateVariationalDistribution  use parameterized QuantumCircuit MultivariateVariationalDistribution  use parameterized QuantumCircuit  UncertaintyProblem   UnivariateProblem   MultivariateProblem   UnivariatePiecewiseLinearObjective  qiskit.circuit.library.LinearAmplitudeFunction

The ising convert classes
qiskit.optimization.converters.QuadraticProgramToIsing
andqiskit.optimization.converters.IsingToQuadraticProgram
have been deprecated and will be removed in a future release. Instead theqiskit.optimization.QuadraticProgram
methodsto_ising()
andfrom_ising()
should be used instead. 
Deprecate the
WeightedSumOperator
which has been ported to the circuit library asWeightedAdder
inqiskit.circuit.library
. 
Core Hamiltonian
class is deprecated in favor of theFermionicTransformation
Chemistry Operator
class is deprecated in favor of thetranformations
minimum_eigen_solvers/vqe_adapt
is also deprecated and moved as an implementation of the ground_state_solver interfaceapplications/molecular_ground_state_energy
is deprecated in favor ofground_state_solver

Optimizer.SupportLevel
nested enum is replaced byOptimizerSupportLevel
andOptimizer.SupportLevel
was removed. Use, for example,OptimizerSupportLevel.required
instead ofOptimizer.SupportLevel.required
. 
Deprecate the
UnivariateVariationalDistribution
andMultivariateVariationalDistribution
as input to theQuantumGenerator
. Instead, plainQuantumCircuit
objects can be used. 
Ignored fast and use_nx options of AbelianGrouper.group_subops to be removed in the future release.

GSLS optimizer class deprecated
__init__
parametermax_iter
in favor ofmaxiter
. SPSA optimizer class deprecated__init__
parametermax_trials
in favor ofmaxiter
. optimize_svm function deprecatedmax_iters
parameter in favor ofmaxiter
. ADMMParameters class deprecated__init__
parametermax_iter
in favor ofmaxiter
.
Bug Fixes
 The UCCSD excitation list, comprising single and double excitations, was not being generated correctly when an active space was explicitly provided to UCSSD via the active_(un)occupied parameters.
 For the amplitude estimation algorithms, we define the number of oracle queries as number of times the Q operator/Grover operator is applied. This includes the number of shots. That factor has been included in MLAE and IQAE but was missing in the ‘standard’ QAE.
 Fix CircuitSampler.convert, so that the
is_measurement
property is propagated to converted StateFns.  Fix double calculation of coefficients in :meth`~qiskit.aqua.operators.VectorStateFn.to_circuit_op`.
 Calling PauliTrotterEvolution.convert on an operator including a term that is a scalar multiple of the identity gave an incorrect circuit, one that ignored the scalar coefficient. This fix includes the effect of the coefficient in the global_phase property of the circuit.
 Make ListOp.num_qubits check that all ops in list have the same num_qubits Previously, the number of qubits in the first operator in the ListOp was returned. With this change, an additional check is made that all other operators also have the same number of qubits.
 Make PauliOp.exp_i() generate the correct matrix with the following changes. 1) There was previously an error in the phase of a factor of 2. 2) The global phase was ignored when converting the circuit to a matrix. We now use qiskit.quantum_info.Operator, which is generally useful for converting a circuit to a unitary matrix, when possible.
 Fixes the cyclicity detection as reported buggy in https://github.com/Qiskit/qiskitaqua/issues/1184(opens in a new tab).
IBM Q Provider 0.11.0
Upgrade Notes
 The deprecated support for running qiskitibmqprovider with Python 3.5 has been removed. To use qiskitibmqprovider >=0.11.0 you will now need at least Python 3.6. If you are using Python 3.5 the last version which will work is qiskitibmqprovider 0.10.x.
 Prior to this release,
websockets
7.0 was used for Python 3.6. With this release,websockets
8.0 or above is required for all Python versions. The package requirements have been updated to reflect this.