Release Notes¶
Version History¶
This table tracks the meta-package versions and the version of each Qiskit element installed:
Qiskit Metapackage Version |
qiskit-terra |
qiskit-aer |
qiskit-ignis |
qiskit-ibmq-provider |
qiskit-aqua |
|---|---|---|---|---|---|
0.29.0 |
0.18.1 |
0.8.2 |
0.6.0 |
0.16.0 |
0.9.4 |
0.28.0 |
0.18.0 |
0.8.2 |
0.6.0 |
0.15.0 |
0.9.4 |
0.27.0 |
0.17.4 |
0.8.2 |
0.6.0 |
0.14.0 |
0.9.2 |
0.26.2 |
0.17.4 |
0.8.2 |
0.6.0 |
0.13.1 |
0.9.1 |
0.26.1 |
0.17.4 |
0.8.2 |
0.6.0 |
0.13.1 |
0.9.1 |
0.26.0 |
0.17.3 |
0.8.2 |
0.6.0 |
0.13.1 |
0.9.1 |
0.25.4 |
0.17.2 |
0.8.2 |
0.6.0 |
0.12.3 |
0.9.1 |
0.25.3 |
0.17.1 |
0.8.2 |
0.6.0 |
0.12.3 |
0.9.1 |
0.25.2 |
0.17.1 |
0.8.1 |
0.6.0 |
0.12.3 |
0.9.1 |
0.25.1 |
0.17.1 |
0.8.1 |
0.6.0 |
0.12.2 |
0.9.1 |
0.25.0 |
0.17.0 |
0.8.0 |
0.6.0 |
0.12.2 |
0.9.0 |
0.24.1 |
0.16.4 |
0.7.6 |
0.5.2 |
0.12.2 |
0.8.2 |
0.24.0 |
0.16.4 |
0.7.6 |
0.5.2 |
0.12.1 |
0.8.2 |
0.23.6 |
0.16.4 |
0.7.5 |
0.5.2 |
0.11.1 |
0.8.2 |
0.23.5 |
0.16.4 |
0.7.4 |
0.5.2 |
0.11.1 |
0.8.2 |
0.23.4 |
0.16.3 |
0.7.3 |
0.5.1 |
0.11.1 |
0.8.1 |
0.23.3 |
0.16.2 |
0.7.3 |
0.5.1 |
0.11.1 |
0.8.1 |
0.23.2 |
0.16.1 |
0.7.2 |
0.5.1 |
0.11.1 |
0.8.1 |
0.23.1 |
0.16.1 |
0.7.1 |
0.5.1 |
0.11.1 |
0.8.1 |
0.23.0 |
0.16.0 |
0.7.0 |
0.5.0 |
0.11.0 |
0.8.0 |
0.22.0 |
0.15.2 |
0.6.1 |
0.4.0 |
0.10.0 |
0.7.5 |
0.21.0 |
0.15.2 |
0.6.1 |
0.4.0 |
0.9.0 |
0.7.5 |
0.20.1 |
0.15.2 |
0.6.1 |
0.4.0 |
0.8.0 |
0.7.5 |
0.20.0 |
0.15.1 |
0.6.1 |
0.4.0 |
0.8.0 |
0.7.5 |
0.19.6 |
0.14.2 |
0.5.2 |
0.3.3 |
0.7.2 |
0.7.3 |
0.19.5 |
0.14.2 |
0.5.2 |
0.3.2 |
0.7.2 |
0.7.3 |
0.19.4 |
0.14.2 |
0.5.2 |
0.3.0 |
0.7.2 |
0.7.2 |
0.19.3 |
0.14.1 |
0.5.2 |
0.3.0 |
0.7.2 |
0.7.1 |
0.19.2 |
0.14.1 |
0.5.1 |
0.3.0 |
0.7.1 |
0.7.1 |
0.19.1 |
0.14.1 |
0.5.1 |
0.3.0 |
0.7.0 |
0.7.0 |
0.19.0 |
0.14.0 |
0.5.1 |
0.3.0 |
0.7.0 |
0.7.0 |
0.18.3 |
0.13.0 |
0.5.1 |
0.3.0 |
0.6.1 |
0.6.6 |
0.18.2 |
0.13.0 |
0.5.0 |
0.3.0 |
0.6.1 |
0.6.6 |
0.18.1 |
0.13.0 |
0.5.0 |
0.3.0 |
0.6.0 |
0.6.6 |
0.18.0 |
0.13.0 |
0.5.0 |
0.3.0 |
0.6.0 |
0.6.5 |
0.17.0 |
0.12.0 |
0.4.1 |
0.2.0 |
0.6.0 |
0.6.5 |
0.16.2 |
0.12.0 |
0.4.1 |
0.2.0 |
0.5.0 |
0.6.5 |
0.16.1 |
0.12.0 |
0.4.1 |
0.2.0 |
0.5.0 |
0.6.4 |
0.16.0 |
0.12.0 |
0.4.0 |
0.2.0 |
0.5.0 |
0.6.4 |
0.15.0 |
0.12.0 |
0.4.0 |
0.2.0 |
0.4.6 |
0.6.4 |
0.14.1 |
0.11.1 |
0.3.4 |
0.2.0 |
0.4.5 |
0.6.2 |
0.14.0 |
0.11.0 |
0.3.4 |
0.2.0 |
0.4.4 |
0.6.1 |
0.13.0 |
0.10.0 |
0.3.2 |
0.2.0 |
0.3.3 |
0.6.1 |
0.12.2 |
0.9.1 |
0.3.0 |
0.2.0 |
0.3.3 |
0.6.0 |
0.12.1 |
0.9.0 |
0.3.0 |
0.2.0 |
0.3.3 |
0.6.0 |
0.12.0 |
0.9.0 |
0.3.0 |
0.2.0 |
0.3.2 |
0.6.0 |
0.11.2 |
0.8.2 |
0.2.3 |
0.1.1 |
0.3.2 |
0.5.5 |
0.11.1 |
0.8.2 |
0.2.3 |
0.1.1 |
0.3.1 |
0.5.3 |
0.11.0 |
0.8.2 |
0.2.3 |
0.1.1 |
0.3.0 |
0.5.2 |
0.10.5 |
0.8.2 |
0.2.1 |
0.1.1 |
0.2.2 |
0.5.2 |
0.10.4 |
0.8.2 |
0.2.1 |
0.1.1 |
0.2.2 |
0.5.1 |
0.10.3 |
0.8.1 |
0.2.1 |
0.1.1 |
0.2.2 |
0.5.1 |
0.10.2 |
0.8.0 |
0.2.1 |
0.1.1 |
0.2.2 |
0.5.1 |
0.10.1 |
0.8.0 |
0.2.0 |
0.1.1 |
0.2.2 |
0.5.0 |
0.10.0 |
0.8.0 |
0.2.0 |
0.1.1 |
0.2.1 |
0.5.0 |
0.9.0 |
0.8.0 |
0.2.0 |
0.1.1 |
0.1.1 |
0.5.0 |
0.8.1 |
0.7.2 |
0.1.1 |
0.1.0 |
||
0.8.0 |
0.7.1 |
0.1.1 |
0.1.0 |
||
0.7.3 |
>=0.7,<0.8 |
>=0.1,<0.2 |
|||
0.7.2 |
>=0.7,<0.8 |
>=0.1,<0.2 |
|||
0.7.1 |
>=0.7,<0.8 |
>=0.1,<0.2 |
|||
0.7.0 |
>=0.7,<0.8 |
>=0.1,<0.2 |
Note
For the 0.7.0, 0.7.1, and 0.7.2 meta-package releases the
Versioning policy was not formalized yet.
Notable Changes¶
Qiskit 0.19.6¶
Terra 0.14.2¶
No Change
Aer 0.5.2¶
No Change
Ignis 0.3.3¶
Upgrade Notes¶
A new requirement scikit-learn has been added to the requirements list. This dependency was added in the 0.3.0 release but wasn’t properly exposed as a dependency in that release. This would lead to an
ImportErrorif theqiskit.ignis.measurement.discriminator.iq_discriminatorsmodule was imported. This is now correctly listed as a dependency so thatscikit-learnwill be installed with qiskit-ignis.
Bug Fixes¶
Fixes an issue in qiskit-ignis 0.3.2 which would raise an
ImportErrorwhenqiskit.ignis.verification.tomography.fitters.process_fitterwas imported withoutcvxpybeing installed.
Aqua 0.7.3¶
No Change
IBM Q Provider 0.7.2¶
No Change
Qiskit 0.19.5¶
Terra 0.14.2¶
No Change
Aer 0.5.2¶
No Change
Ignis 0.3.2¶
Bug Fixes¶
The
qiskit.ignis.verification.TomographyFitter.fit()method has improved detection logic for the default fitter. Previously, thecvxfitter method was used whenever cvxpy was installed. However, it was possible to install cvxpy without an SDP solver that would work for thecvxfitter method. This logic has been reworked so that thecvxfitter method is only used ifcvxpyis installed and an SDP solver is present that can be used. Otherwise, thelstsqfitter is used.Fixes an edge case in
qiskit.ignis.mitigation.measurement.fitters.MeasurementFitter.apply()for input that has invalid or incorrect state labels that don’t match the calibration circuit. Previously, this would not error and just return an empty result. Instead now this case is correctly caught and aQiskitErrorexception is raised when using incorrect labels.
Aqua 0.7.3¶
Upgrade Notes¶
The cvxpy dependency which is required for the svm classifier has been removed from the requirements list and made an optional dependency. This is because installing cvxpy is not seamless in every environment and often requires a compiler be installed to run. To use the svm classifier now you’ll need to install cvxpy by either running
pip install cvxpy<1.1.0or to install it with aqua runningpip install qiskit-aqua[cvx].
Bug Fixes¶
The
composemethod of theCircuitOpusedQuantumCircuit.combinewhich has been changed to useQuantumCircuit.compose. Using combine leads to the problem that composing an operator with aCircuitOpbased on a named register does not chain the operators but stacks them. E.g. composingZ ^ 2with a circuit based on a 2-qubit named register yielded a 4-qubit operator instead of a 2-qubit operator.The
MatrixOp.to_instructionmethod previously returned an operator and not an instruction. This method has been updated to return an Instruction. Note that this only works if the operator primitive is unitary, otherwise an error is raised upon the construction of the instruction.The
__hash__method of thePauliOpclass used theid()method which prevents set comparisons to work as expected since they rely on hash tables and identical objects used to not have identical hashes. Now, the implementation uses a hash of the string representation inline with the implementation in thePauliclass.
IBM Q Provider 0.7.2¶
No Change
Qiskit 0.19.4¶
Terra 0.14.2¶
Upgrade Notes¶
The
circuit_to_gateandcircuit_to_instructionconverters had previously automatically included the generated gate or instruction in the activeSessionEquivalenceLibrary. These converters now accept an optionalequivalence_librarykeyword argument to specify if and where the converted instances should be registered. The default behavior is not to register the converted instance.
Bug Fixes¶
Implementations of the multi-controlled X Gate (
MCXGrayCode,MCXRecursiveandMCXVChain) have had theirnameproperties changed to more accurately describe their implementation (mcx_gray,mcx_recursive, andmcx_vchainrespectively.) Previously, these gates shared the namemcx` with ``MCXGate, which caused these gates to be incorrectly transpiled and simulated.ControlledGateinstances with a setctrl_statewere in some cases not being evaluated as equal, even if the compared gates were equivalent. This has been resolved.Fixed the SI unit conversion for
qiskit.pulse.SetFrequency. TheSetFrequencyinstruction should be in Hz on the frontend and has to be converted to GHz whenSetFrequencyis converted toPulseQobjInstruction.Open controls were implemented by modifying a gate's definition. However, when the gate already exists in the basis, this definition is not used, which yields incorrect circuits sent to a backend. This modifies the unroller to output the definition if it encounters a controlled gate with open controls.
Aer 0.5.2¶
No Change
Ignis 0.3.0¶
No Change
Aqua 0.7.2¶
Prelude¶
VQE expectation computation with Aer qasm_simulator now defaults to a computation that has the expected shot noise behavior.
Upgrade Notes¶
cvxpy is now in the requirements list as a dependency for qiskit-aqua. It is used for the quadratic program solver which is used as part of the
qiskit.aqua.algorithms.QSVM. Previouslycvxoptwas an optional dependency that needed to be installed to use this functionality. This is no longer required as cvxpy will be installed with qiskit-aqua.For state tomography run as part of
qiskit.aqua.algorithms.HHLwith a QASM backend the tomography fitter functionqiskit.ignis.verification.StateTomographyFitter.fit()now gets called explicitly with the method set tolstsqto always use the least-squares fitting. Previously it would opportunistically try to use thecvxfitter ifcvxpywere installed. But, thecvxfitter depends on a specifically configuredcvxpyinstallation with an SDP solver installed as part ofcvxpywhich is not always present in an environment withcvxpyinstalled.The VQE expectation computation using qiskit-aer’s
qiskit.providers.aer.extensions.SnapshotExpectationValueinstruction is not enabled by default anymore. This was changed to be the default in 0.7.0 because it is significantly faster, but it led to unexpected ideal results without shot noise (see #1013 for more details). The default has now changed back to match user expectations. Using the faster expectation computation is now opt-in by setting the newinclude_customkwarg toTrueon theqiskit.aqua.algorithms.VQEconstructor.
New Features¶
A new kwarg
include_customhas been added to the constructor forqiskit.aqua.algorithms.VQEand it’s subclasses (mainlyqiskit.aqua.algorithms.QAOA). When set to true and theexpectationkwarg is set toNone(the default) this will enable the use of VQE expectation computation with Aer’sqasm_simulatorqiskit.providers.aer.extensions.SnapshotExpectationValueinstruction. The special Aer snapshot based computation is much faster but with the ideal output similar to state vector simulator.
IBM Q Provider 0.7.2¶
No Change
Qiskit 0.19.3¶
Terra 0.14.1¶
No Change
Aer 0.5.2¶
Bug Fixes¶
Fixed bug with statevector and unitary simulators running a number of (parallel) shots equal to the number of CPU threads instead of only running a single shot.
Fixes the “diagonal” qobj gate instructions being applied incorrectly in the density matrix Qasm Simulator method.
Fixes bug where conditional gates were not being applied correctly on the density matrix simulation method.
Fix bug in CZ gate and Z gate for “density_matrix_gpu” and “density_matrix_thrust” QasmSimulator methods.
Fixes issue where memory requirements of simulation were not being checked on the QasmSimulator when using a non-automatic simulation method.
Fixed a memory leak that effected the GPU simulator methods
Ignis 0.3.0¶
No Change
Aqua 0.7.1¶
No Change
IBM Q Provider 0.7.2¶
Bug Fixes¶
qiskit.provider.ibmq.IBMQBackend.jobs()will now return the correct list ofIBMQJobobjects when thestatuskwarg is set to'RUNNING'. Fixes #523The package metadata has been updated to properly reflect the dependency on
qiskit-terra>= 0.14.0. This dependency was implicitly added as part of the 0.7.0 release but was not reflected in the package requirements so it was previously possible to installqiskit-ibmq-providerwith a version ofqiskit-terrawhich was too old. Fixes #677
Qiskit 0.19.0¶
Terra 0.14.0¶
Prelude¶
The 0.14.0 release includes several new features and bug fixes. The biggest
change for this release is the introduction of a quantum circuit library
in qiskit.circuit.library, containing some circuit families of
interest.
The circuit library gives users access to a rich set of well-studied circuit families, instances of which can be used as benchmarks, as building blocks in building more complex circuits, or as a tool to explore quantum computational advantage over classical. The contents of this library will continue to grow and mature.
The initial release of the circuit library contains:
standard_gates: these are fixed-width gates commonly used as primitive building blocks, consisting of 1, 2, and 3 qubit gates. For example theXGate,RZZGateandCSWAPGate. The old location of these gates underqiskit.extensions.standardis deprecated.generalized_gates: these are families that can generalize to arbitrarily many qubits, for example aPermutationorGMS(Global Molmer-Sorensen gate).boolean_logic: circuits that transform basis states according to simple Boolean logic functions, such asADDorXOR.arithmetic: a set of circuits for doing classical arithmetic such asWeightedAdderandIntegerComparator.basis_changes: circuits such as the quantum Fourier transform,QFT, that mathematically apply basis changes.n_local: patterns to easily create large circuits with rotation and entanglement layers, such asTwoLocalwhich uses single-qubit rotations and two-qubit entanglements.data_preparation: circuits that take classical input data and encode it in a quantum state that is difficult to simulate, e.g.PauliFeatureMaporZZFeatureMap.Other circuits that have proven interesting in the literature, such as
QuantumVolume,GraphState, orIQP.
To allow easier use of these circuits as building blocks, we have introduced
a compose() method of
qiskit.circuit.QuantumCircuit for composition of circuits either
with other circuits (by welding them at the ends and optionally permuting
wires) or with other simpler gates:
>>> lhs.compose(rhs, qubits=[3, 2], inplace=True)
┌───┐ ┌─────┐ ┌───┐
lqr_1_0: ───┤ H ├─── rqr_0: ──■──┤ Tdg ├ lqr_1_0: ───┤ H ├───────────────
├───┤ ┌─┴─┐└─────┘ ├───┤
lqr_1_1: ───┤ X ├─── rqr_1: ┤ X ├─────── lqr_1_1: ───┤ X ├───────────────
┌──┴───┴──┐ └───┘ ┌──┴───┴──┐┌───┐
lqr_1_2: ┤ U1(0.1) ├ + = lqr_1_2: ┤ U1(0.1) ├┤ X ├───────
└─────────┘ └─────────┘└─┬─┘┌─────┐
lqr_2_0: ─────■───── lqr_2_0: ─────■───────■──┤ Tdg ├
┌─┴─┐ ┌─┴─┐ └─────┘
lqr_2_1: ───┤ X ├─── lqr_2_1: ───┤ X ├───────────────
└───┘ └───┘
lcr_0: 0 ═══════════ lcr_0: 0 ═══════════════════════
lcr_1: 0 ═══════════ lcr_1: 0 ═══════════════════════
With this, Qiskit’s circuits no longer assume an implicit initial state of \(|0\rangle\), and will not be drawn with this initial state. The all-zero initial state is still assumed on a backend when a circuit is executed.
New Features¶
A new method,
has_entry(), has been added to theqiskit.circuit.EquivalenceLibraryclass to quickly check if a given gate has any known decompositions in the library.A new class
IQP, to construct an instantaneous quantum polynomial circuit, has been added to the circuit library moduleqiskit.circuit.library.A new
compose()method has been added toqiskit.circuit.QuantumCircuit. It allows composition of two quantum circuits without having to turn one into a gate or instruction. It also allows permutations of qubits/clbits at the point of composition, as well as optional inplace modification. It can also be used in place ofappend(), as it allows composing instructions and operators onto the circuit as well.qiskit.circuit.library.Diagonalcircuits have been added to the circuit library. These circuits implement diagonal quantum operators (consisting of non-zero elements only on the diagonal). They are more efficiently simulated by the Aer simulator than dense matrices.Add
from_label()method to theqiskit.quantum_info.Cliffordclass for initializing as the tensor product of single-qubit I, X, Y, Z, H, or S gates.Schedule transformer
qiskit.pulse.reschedule.compress_pulses()performs an optimization pass to reduce the usage of waveform memory in hardware by replacing multiple identical instances of a pulse in a pulse schedule with a single pulse. For example:from qiskit.pulse import reschedule schedules = [] for _ in range(2): schedule = Schedule() drive_channel = DriveChannel(0) schedule += Play(SamplePulse([0.0, 0.1]), drive_channel) schedule += Play(SamplePulse([0.0, 0.1]), drive_channel) schedules.append(schedule) compressed_schedules = reschedule.compress_pulses(schedules)
The
qiskit.transpiler.Layouthas a new methodreorder_bits()that is used to reorder a list of virtual qubits based on the layout object.Two new methods have been added to the
qiskit.providers.models.PulseBackendConfigurationfor interacting with channels.get_channel_qubits()to get a list of all qubits operated by the given channel andget_qubit_channel()to get a list of channels operating on the given qubit.
New
qiskit.extensions.HamiltonianGateandqiskit.circuit.QuantumCircuit.hamiltonian()methods are introduced, representing Hamiltonian evolution of the circuit wavefunction by a user-specified Hermitian Operator and evolution time. The evolution time can be aParameter, allowing the creation of parameterized UCCSD or QAOA-style circuits which compile toUnitaryGateobjects iftimeparameters are provided. The Unitary of aHamiltonianGatewith Hamiltonian OperatorHand time parametertis \(e^{-iHt}\).The circuit library module
qiskit.circuit.librarynow provides a new boolean logic AND circuit,qiskit.circuit.library.AND, and OR circuit,qiskit.circuit.library.OR, which implement the respective operations on a variable number of provided qubits.New fake backends are added under
qiskit.test.mock. These include mocked versions ofibmq_armonk,ibmq_essex,ibmq_london,ibmq_valencia,ibmq_cambridge,ibmq_paris,ibmq_rome, andibmq_athens. As with other fake backends, these include snapshots of calibration data (i.e.backend.defaults()) and error data (i.e.backend.properties()) taken from the real system, and can be used for local testing, compilation and simulation.The
last_update_dateparameter forBackendPropertiescan now also be passed in as adatetimeobject. Previously only a string in ISO8601 format was accepted.Adds
qiskit.quantum_info.Statevector.from_int()andqiskit.quantum_info.DensityMatrix.from_int()methods that allow constructing a computational basis state for specified system dimensions.The methods on the
qiskit.circuit.QuantumCircuitclass for adding gates (for exampleh()) which were previously added dynamically at run time to the class definition have been refactored to be statically defined methods of the class. This means that static analyzer (such as IDEs) can now read these methods.
Upgrade Notes¶
A new package, python-dateutil, is now required and has been added to the requirements list. It is being used to parse datetime strings received from external providers in
BackendPropertiesobjects.The marshmallow schema classes in
qiskit.providers.modelshave been removed since they are no longer used by the BackendObjects.The output of the
to_dict()method for the classes inqiskit.providers.modelsis no longer in a format for direct JSON serialization. Depending on the content contained in instances of these class there may be numpy arrays and/or complex numbers in the fields of the dict. If you’re JSON serializing the output of the to_dict methods you should ensure your JSON encoder can handle numpy arrays and complex numbers. This includes:qiskit.providers.models.Nduv.to_dict()qiskit.providers.models.Gate.to_dict()
Deprecation Notes¶
The
qiskit.dagcircuit.DAGCircuit.compose()method now takes a list of qubits/clbits that specify the positional order of bits to compose onto. The dictionary-based method of mapping using theedge_mapargument is deprecated and will be removed in a future release.The
combine_into_edge_map()method for theqiskit.transpiler.Layoutclass has been deprecated and will be removed in a future release. Instead, the new methodreorder_bits()should be used to reorder a list of virtual qubits according to the layout object.Passing a
qiskit.pulse.ControlChannelobject in via the parameterchannelfor theqiskit.providers.models.PulseBackendConfigurationmethodcontrol()has been deprecated and will be removed in a future release. TheControlChannelobjects are now generated from the backend configurationchannelsattribute which has the information of all channels and the qubits they operate on. Now, the methodcontrol()is expected to take the parameterqubitsof the form(control_qubit, target_qubit)and typelistortuple, and returns a list of control channels.The
ANDandORmethods ofqiskit.circuit.QuantumCircuitare deprecated and will be removed in a future release. Instead you should use the circuit library boolean logic classesqiskit.circuit.library.ANDamdqiskit.circuit.library.ORand then append those objects to your class. For example:from qiskit import QuantumCircuit from qiskit.circuit.library import AND qc = QuantumCircuit(2) qc.h(0) qc.cx(0, 1) qc_and = AND(2) qc.compose(qc_and, inplace=True)
The
qiskit.extensions.standardmodule is deprecated and will be removed in a future release. The gate classes in that module have been moved toqiskit.circuit.library.standard_gates.
Bug Fixes¶
The
qiskit.circuit.QuantumCircuitmethodsinverse(),mirror()methods, as well as theQuantumCircuit.datasetter would generate an invalid circuit when used on a parameterized circuit instance. This has been resolved and these methods should now work with a parameterized circuit. Fixes #4235Previously when creating a controlled version of a standard qiskit gate if a
ctrl_statewas specified a genericControlledGateobject would be returned whereas without it a standard qiskit controlled gate would be returned if it was defined. This PR allows standard qiskit controlled gates to understandctrl_state.Additionally, this PR fixes what might be considered a bug where setting the
ctrl_stateof an already controlled gate would assume the specified state applied to the full control width instead of the control qubits being added. For instance,:circ = QuantumCircuit(2) circ.h(0) circ.x(1) gate = circ.to_gate() cgate = gate.control(1) c3gate = cgate.control(2, ctrl_state=0)
would apply
ctrl_stateto all three control qubits instead of just the two control qubits being added.Fixed a bug in
random_clifford()that stopped it from sampling the full Clifford group. Fixes #4271The
qiskit.circuit.Instructionmethodqiskit.circuit.Instruction.is_parameterized()method had previously returnedTruefor anyInstructioninstance which had aqiskit.circuit.Parameterin any element of itsparamsarray, even if thatParameterhad been fully bound. This has been corrected so that.is_parameterizedwill returnFalsewhen the instruction is fully bound.qiskit.circuit.ParameterExpression.subs()had not correctly detected some cases where substituting parameters would result in a two distinctParametersobjects in an expression with the same name. This has been corrected so aCircuitErrorwill be raised in these cases.Improve performance of
qiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixfor low-qubit circuit simulations by optimizing the class__init__methods. Fixes #4281The function
qiskit.compiler.transpile()now correctly handles when the parameterbasis_gatesis set toNone. This will allow any gate in the output tranpiled circuit, including gates added by the transpilation process. Note that using this parameter may have some unintended consequences during optimization. Some transpiler passes depend on having abasis_gatesset. For example,qiskit.transpiler.passes.Optimize1qGatesonly optimizes the chains of u1, u2, and u3 gates and withoutbasis_gatesit is unable to unroll gates that otherwise could be optimized:from qiskit import * q = QuantumRegister(1, name='q') circuit = QuantumCircuit(q) circuit.h(q[0]) circuit.u1(0.1, q[0]) circuit.u2(0.1, 0.2, q[0]) circuit.h(q[0]) circuit.u3(0.1, 0.2, 0.3, q[0]) result = transpile(circuit, basis_gates=None, optimization_level=3) result.draw()
┌───┐┌─────────────┐┌───┐┌─────────────────┐ q_0: ┤ H ├┤ U2(0.1,0.3) ├┤ H ├┤ U3(0.1,0.2,0.3) ├ └───┘└─────────────┘└───┘└─────────────────┘Fixes #3017
Other Notes¶
The objects in
qiskit.providers.modelswhich were previously constructed using the marshmallow library have been refactored to not depend on marshmallow. This includes:NduvGate
These should be drop-in replacements without any noticeable change but specifics inherited from marshmallow may not work. Please file issues for any incompatibilities found.
Aer 0.5.1¶
No Change
Ignis 0.3.0¶
No Change
Aqua 0.7.0¶
Prelude¶
The Qiskit Aqua 0.7.0 release introduces a lot of new functionality along
with an improved integration with qiskit.circuit.QuantumCircuit
objects. The central contributions are the Qiskit’s optimization module,
a complete refactor on Operators, using circuits as native input for the
algorithms and removal of the declarative JSON API.
Optimization module¶
The qiskit.optimization` module now offers functionality for modeling
and solving quadratic programs. It provides various near-term quantum and
conventional algorithms, such as the MinimumEigenOptimizer
(covering e.g. VQE or QAOA) or CplexOptimizer, as well as
a set of converters to translate between different
problem representations, such as QuadraticProgramToQubo.
See the
changelog
for a list of the added features.
Operator flow¶
The operator logic provided in qiskit.aqua.operators` was completely
refactored and is now a full set of tools for constructing
physically-intuitive quantum computations. It contains state functions,
operators and measurements and internally relies on Terra’s Operator
objects. Computing expectation values and evolutions was heavily simplified
and objects like the ExpectationFactory produce the suitable, most
efficient expectation algorithm based on the Operator input type.
See the changelog
for a overview of the added functionality.
Native circuits¶
Algorithms commonly use parameterized circuits as input, for example the
VQE, VQC or QSVM. Previously, these inputs had to be of type
VariationalForm or FeatureMap which were wrapping the circuit
object. Now circuits are natively supported in these algorithms, which
means any individually constructed QuantumCircuit can be passed to
these algorithms. In combination with the release of the circuit library
which offers a wide collection of circuit families, it is now easy to
construct elaborate circuits as algorithm input.
Declarative JSON API¶
The ability of running algorithms using dictionaries as parameters as well as using the Aqua interfaces GUI has been removed.
IBM Q Provider 0.7.0¶
New Features¶
A new exception,
qiskit.providers.ibmq.IBMQBackendJobLimitError, is now raised if a job could not be submitted because the limit on active jobs has been reached.qiskit.providers.ibmq.job.IBMQJobandqiskit.providers.ibmq.managed.ManagedJobSeteach has two new methodsupdate_nameandupdate_tags. They are used to change the name and tags of a job or a job set, respectively.qiskit.providers.ibmq.IBMQFactory.save_account()andqiskit.providers.ibmq.IBMQFactory.enable_account()now accept optional parametershub,group, andproject, which allow specifying a default provider to save to disk or use, respectively.
Upgrade Notes¶
The
qiskit.providers.ibmq.job.IBMQJobmethodscreation_dateandtime_per_stepnow return date time information as adatetimeobject in local time instead of UTC. Similarly, the parametersstart_datetimeandend_datetime, ofqiskit.providers.ibmq.IBMQBackendService.jobs()andqiskit.providers.ibmq.IBMQBackend.jobs()can now be specified in local time.The
qiskit.providers.ibmq.job.QueueInfo.format()method now uses a customdatetimeto string formatter, and the package arrow is no longer required and has been removed from the requirements list.
Deprecation Notes¶
The
from_dict()andto_dict()methods ofqiskit.providers.ibmq.job.IBMQJobare deprecated and will be removed in the next release.
Bug Fixes¶
Fixed an issue where
nest_asyncio.apply()may raise an exception if there is no asyncio loop due to threading.
Qiskit 0.18.3¶
Terra 0.13.0¶
No Change
Aer 0.5.1¶
Upgrade Notes¶
Changes how transpilation passes are handled in the C++ Controller classes so that each pass must be explicitly called. This allows for greater customization on when each pass should be called, and with what parameters. In particular this enables setting different parameters for the gate fusion optimization pass depending on the QasmController simulation method.
Add
gate_length_unitskwarg toqiskit.providers.aer.noise.NoiseModel.from_device()for specifying customgate_lengthsin the device noise model function to handle unit conversions for internal code.Add Controlled-Y (“cy”) gate to the Stabilizer simulator methods supported gateset.
For Aer’s backend the jsonschema validation of input qobj objects from terra is now opt-in instead of being enabled by default. If you want to enable jsonschema validation of qobj set the
validatekwarg on theqiskit.providers.aer.QasmSimualtor.run()method for the backend object toTrue.
Bug Fixes¶
Remove “extended_stabilizer” from the automatically selected simulation methods. This is needed as the extended stabilizer method is not exact and may give incorrect results for certain circuits unless the user knows how to optimize its configuration parameters.
The automatic method now only selects from “stabilizer”, “density_matrix”, and “statevector” methods. If a non-Clifford circuit that is too large for the statevector method is executed an exception will be raised suggesting you could try explicitly using the “extended_stabilizer” or “matrix_product_state” methods instead.
Fixes Controller classes so that the ReduceBarrier transpilation pass is applied first. This prevents barrier instructions from preventing truncation of unused qubits if the only instruction defined on them was a barrier.
Disables gate fusion for the matrix product state simulation method as this was causing issues with incorrect results being returned in some cases.
Fix error in gate time unit conversion for device noise model with thermal relaxation errors and gate errors. The error probability the depolarizing error was being calculated with gate time in microseconds, while for thermal relaxation it was being calculated in nanoseconds. This resulted in no depolarizing error being applied as the incorrect units would make the device seem to be coherence limited.
Fix bug in incorrect composition of QuantumErrors when the qubits of composed instructions differ.
Fix issue where the “diagonal” gate is checked to be unitary with too high a tolerance. This was causing diagonals generated from Numpy functions to often fail the test.
Fix remove-barrier circuit optimization pass to be applied before qubit trucation. This fixes an issue where barriers inserted by the Terra transpiler across otherwise inactive qubits would prevent them from being truncated.
Ignis 0.3.0¶
No Change
Aqua 0.6.6¶
No Change
IBM Q Provider 0.6.1¶
No Change
Qiskit 0.18.0¶
Terra 0.13.0¶
Prelude¶
The 0.13.0 release includes many big changes. Some highlights for this release are:
For the transpiler we have switched the graph library used to build the
qiskit.dagcircuit.DAGCircuit class which is the underlying data
structure behind all operations to be based on
retworkx for greatly improved
performance. Circuit transpilation speed in the 0.13.0 release should
be significanlty faster than in previous releases.
There has been a significant simplification to the style in which Pulse
instructions are built. Now, Command s are deprecated and a unified
set of Instruction s are supported.
The qiskit.quantum_info module includes several new functions
for generating random operators (such as Cliffords and quantum channels)
and for computing the diamond norm of quantum channels; upgrades to the
Statevector and
DensityMatrix classes to support
computing measurement probabilities and sampling measurements; and several
new classes are based on the symplectic representation
of Pauli matrices. These new classes include Clifford operators
(Clifford), N-qubit matrices that are
sparse in the Pauli basis (SparsePauliOp),
lists of Pauli’s (PauliTable),
and lists of stabilizers (StabilizerTable).
This release also has vastly improved documentation across Qiskit,
including improved documentation for the qiskit.circuit,
qiskit.pulse and qiskit.quantum_info modules.
Additionally, the naming of gate objects and
QuantumCircuit methods have been updated to be
more consistent. This has resulted in several classes and methods being
deprecated as things move to a more consistent naming scheme.
For full details on all the changes made in this release see the detailed release notes below.
New Features¶
Added a new circuit library module
qiskit.circuit.library. This will be a place for constructors of commonly used circuits that can be used as building blocks for larger circuits or applications.The
qiskit.providers.BaseJobclass has four new methods:These methods are used to check wheter a job is in a given job status.
Add ability to specify control conditioned on a qubit being in the ground state. The state of the control qubits is represented by an integer. For example:
from qiskit import QuantumCircuit from qiskit.extensions.standard import XGate qc = QuantumCircuit(4) cgate = XGate().control(3, ctrl_state=6) qc.append(cgate, [0, 1, 2, 3])
Creates a four qubit gate where the fourth qubit gets flipped if the first qubit is in the ground state and the second and third qubits are in the excited state. If
ctrl_stateisNone, the default, control is conditioned on all control qubits being excited.A new jupyter widget,
%circuit_library_infohas been added toqiskit.tools.jupyter. This widget is used for visualizing details about circuits built from the circuit library. For examplefrom qiskit.circuit.library import XOR import qiskit.tools.jupyter circuit = XOR(5, seed=42) %circuit_library_info circuit
A new kwarg option,
formatted, has been added toqiskit.circuit.QuantumCircuit.qasm(). When set toTruethe method will print a syntax highlighted version (using pygments) to stdout and returnNone(which differs from the normal behavior of returning the QASM code as a string).A new kwarg option,
filename, has been added toqiskit.circuit.QuantumCircuit.qasm(). When set to a path the method will write the QASM code to that file. It will then continue to output as normal.A new instruction
SetFrequencywhich allows users to change the frequency of thePulseChannel. This is done in the following way:from qiskit.pulse import Schedule from qiskit.pulse import SetFrequency sched = pulse.Schedule() sched += SetFrequency(5.5e9, DriveChannel(0))
In this example, the frequency of all pulses before the
SetFrequencycommand will be the default frequency and all pulses applied to drive channel zero after theSetFrequencycommand will be at 5.5 GHz. Users ofSetFrequencyshould keep in mind any hardware limitations.A new method,
assign_parameters()has been added to theqiskit.circuit.QuantumCircuitclass. This method accepts a parameter dictionary with both floats and Parameters objects in a single dictionary. In other words this new method allows you to bind floats, Parameters or both in a single dictionary.Also, by using the
inplacekwarg it can be specified you can optionally modify the original circuit in place. By default this is set toFalseand a copy of the original circuit will be returned from the method.A new method
num_nonlocal_gates()has been added to theqiskit.circuit.QuantumCircuitclass. This method will return the number of gates in a circuit that involve 2 or or more qubits. These gates are more costly in terms of time and error to implement.The
qiskit.circuit.QuantumCircuitmethodiso()for adding anIsometrygate to the circuit has a new alias. You can now callqiskit.circuit.QuantumCircuit.isometry()in addition to callingiso.A
descriptionattribute has been added to theCouplingMapclass for storing a short description for different coupling maps (e.g. full, grid, line, etc.).A new method
compose()has been added to theDAGCircuitclass for composing two circuits via their DAGs.dag_left.compose(dag_right, edge_map={right_qubit0: self.left_qubit1, right_qubit1: self.left_qubit4, right_clbit0: self.left_clbit1, right_clbit1: self.left_clbit0})
┌───┐ ┌─────┐┌─┐ lqr_1_0: ───┤ H ├─── rqr_0: ──■──┤ Tdg ├┤M├ ├───┤ ┌─┴─┐└─┬─┬─┘└╥┘ lqr_1_1: ───┤ X ├─── rqr_1: ┤ X ├──┤M├───╫─ ┌──┴───┴──┐ └───┘ └╥┘ ║ lqr_1_2: ┤ U1(0.1) ├ + rcr_0: ════════╬════╩═ = └─────────┘ ║ lqr_2_0: ─────■───── rcr_1: ════════╩══════ ┌─┴─┐ lqr_2_1: ───┤ X ├─── └───┘ lcr_0: ═══════════ lcr_1: ═══════════ ┌───┐ lqr_1_0: ───┤ H ├────────────────── ├───┤ ┌─────┐┌─┐ lqr_1_1: ───┤ X ├─────■──┤ Tdg ├┤M├ ┌──┴───┴──┐ │ └─────┘└╥┘ lqr_1_2: ┤ U1(0.1) ├──┼──────────╫─ └─────────┘ │ ║ lqr_2_0: ─────■───────┼──────────╫─ ┌─┴─┐ ┌─┴─┐ ┌─┐ ║ lqr_2_1: ───┤ X ├───┤ X ├──┤M├───╫─ └───┘ └───┘ └╥┘ ║ lcr_0: ═══════════════════╩════╬═ ║ lcr_1: ════════════════════════╩═The mock backends in
qiskit.test.mocknow have a functionalrun()method that will return results similar to the real devices. Ifqiskit-aeris installed a simulation will be run with a noise model built from the device snapshot in the fake backend. Otherwise,qiskit.providers.basicaer.QasmSimulatorPywill be used to run an ideal simulation. Additionally, if a pulse experiment is passed torunand qiskit-aer is installed thePulseSimulatorwill be used to simulate the pulse schedules.The
qiskit.result.Result()methodget_counts()will now return a list of all the counts available when there are multiple circuits in a job. This works whenget_counts()is called with no arguments.The main consideration for this feature was for drawing all the results from multiple circuits in the same histogram. For example it is now possible to do something like:
from qiskit import execute from qiskit import QuantumCircuit from qiskit.providers.basicaer import BasicAer from qiskit.visualization import plot_histogram sim = BasicAer.get_backend('qasm_simulator') qc = QuantumCircuit(2) qc.h(0) qc.cx(0, 1) qc.measure_all() result = execute([qc, qc, qc], sim).result() plot_histogram(result.get_counts())
A new kwarg,
initial_statehas been added to theqiskit.visualization.circuit_drawer()function and theQuantumCircuitmethoddraw(). When set toTruethe initial state will be included in circuit visualizations for all backends. For example:from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.measure_all() circuit.draw(output='mpl', initial_state=True)
It is now possible to insert a callable into a
qiskit.pulse.InstructionScheduleMapwhich returns a newqiskit.pulse.Schedulewhen it is called with parameters. For example:def test_func(x): sched = Schedule() sched += pulse_lib.constant(int(x), amp_test)(DriveChannel(0)) return sched inst_map = InstructionScheduleMap() inst_map.add('f', (0,), test_func) output_sched = inst_map.get('f', (0,), 10) assert output_sched.duration == 10
Two new gate classes,
qiskit.extensions.iSwapGateandqiskit.extensions.DCXGate, along with theirQuantumCircuitmethodsiswap()anddcx()have been added to the standard extensions. These gates, which are locally equivalent to each other, can be used to enact particular XY interactions. A brief motivation for these gates can be found in: arxiv.org/abs/quant-ph/0209035The
qiskit.providers.BaseJobclass now has a new methodwait_for_final_state()that polls for the job status until the job reaches a final state (such asDONEorERROR). This method also takes an optionalcallbackkwarg which takes a Python callable that will be called during each iteration of the poll loop.The
search_widthandsearch_depthattributes of theqiskit.transpiler.passes.LookaheadSwappass are now settable when initializing the pass. A larger search space can often lead to more optimized circuits, at the cost of longer run time.The number of qubits in
BackendConfigurationcan now be accessed via the propertynum_qubits. It was previously only accessible via then_qubitsattribute.Two new methods,
angles()andangles_and_phase(), have been added to theqiskit.quantum_info.OneQubitEulerDecomposerclass. These methods will return the relevant parameters without validation, and calling theOneQubitEulerDecomposerobject will perform the full synthesis with validation.An
RRdecomposition basis has been added to theqiskit.quantum_info.OneQubitEulerDecomposerfor decomposing an arbitrary 2x2 unitary into a twoRGatecircuit.Adds the ability to set
qargsto objects which are subclasses of the abstractBaseOperatorclass. This is done by calling the objectop(qargs)(whereopis an operator class) and will return a shallow copy of the original object with a qargs property set. When such an object is used with thecompose()ordot()methods the internal value for qargs will be used when theqargsmethod kwarg is not used. This allows for subsystem composition using binary operators, for example:from qiskit.quantum_info import Operator init = Operator.from_label('III') x = Operator.from_label('X') h = Operator.from_label('H') init @ x([0]) @ h([1])
Adds
qiskit.quantum_info.Cliffordoperator class to the quantum_info module. This operator is an efficient symplectic representation an N-qubit unitary operator from the Clifford group. This class includes ato_circuit()method for compilation into aQuantumCircuitof Clifford gates with a minimal number of CX gates for up to 3-qubits. It also providers general compilation for N > 3 qubits but this method is not optimal in the number of two-qubit gates.Adds
qiskit.quantum_info.SparsePauliOpoperator class. This is an efficient representaiton of an N-qubit matrix that is sparse in the Pauli basis and uses aqiskit.quantum_info.PauliTableand vector of complex coefficients for its data structure.This class supports much of the same functionality of the
qiskit.quantum_info.Operatorclass soSparsePauliOpobjects can be tensored, composed, scalar multiplied, added and subtracted.Numpy arrays or
Operatorobjects can be converted to aSparsePauliOpusing the :class:`~qiskit.quantum_info.SparsePauliOp.from_operator method.SparsePauliOpcan be convered to a sparse csr_matrix or dense Numpy array using theto_matrixmethod, or to anOperatorobject using theto_operatormethod.A
SparsePauliOpcan be iterated over in terms of itsPauliTablecomponents and coefficients, its coefficients and Pauli string labels using thelabel_iter()method, and the (dense or sparse) matrix components using thematrix_iter()method.Add
qiskit.quantum_info.diamond_norm()function for computing the diamond norm (completely-bounded trace-norm) of a quantum channel. This can be used to compute the distance between two quantum channels usingdiamond_norm(chan1 - chan2).A new class
qiskit.quantum_info.PauliTablehas been added. This is an efficient symplectic representation of a list of N-qubit Pauli operators. Some features of this class are:PauliTableobjects may be composed, and tensored which will return aPauliTableobject with the combination of the operation (compose(),dot(),expand(),tensor()) between each element of the first table, with each element of the second table.Addition of two tables acts as list concatination of the terms in each table (
+).Pauli tables can be sorted by lexicographic (tensor product) order or by Pauli weights (
sort()).Duplicate elements can be counted and deleted (
unique()).The PauliTable may be iterated over in either its native symplectic boolean array representation, as Pauli string labels (
label_iter()), or as dense Numpy array or sparse CSR matrices (matrix_iter()).Checking commutation between elements of the Pauli table and another Pauli (
commutes()) or Pauli table (commutes_with_all())
See the
qiskit.quantum_info.PauliTableclass API documentation for additional details.Adds
qiskit.quantum_info.StabilizerTableclass. This is a subclass of theqiskit.quantum_info.PauliTableclass which includes a boolean phase vector along with the Pauli table array. This represents a list of Stabilizer operators which are real-Pauli operators with +1 or -1 coefficient. Because the stabilizer matrices are real the"Y"label matrix is defined as[[0, 1], [-1, 0]]. See the API documentation for additional information.Adds
qiskit.quantum_info.pauli_basis()function which returns an N-qubit Pauli basis as aqiskit.quantum_info.PauliTableobject. The ordering of this basis can either be by standard lexicographic (tensor product) order, or by the number of non-identity Pauli terms (weight).Adds
qiskit.quantum_info.ScalarOpoperator class that represents a scalar multiple of an identity operator. This can be used to initialize an identity on arbitrary dimension subsystems and it will be implicitly converted to otherBaseOperatorsubclasses (such as anqiskit.quantum_info.Operatororqiskit.quantum_info.SuperOp) when it is composed with, or added to, them.Example: Identity operator
from qiskit.quantum_info import ScalarOp, Operator X = Operator.from_label('X') Z = Operator.from_label('Z') init = ScalarOp(2 ** 3) # 3-qubit identity op = init @ X([0]) @ Z([1]) @ X([2]) # Op XZX
A new method,
reshape(), has been added to theqiskit.quantum_innfo.Operatorclass that returns a shallow copy of an operator subclass with reshaped subsystem input or output dimensions. The combined dimensions of all subsystems must be the same as the original operator or an exception will be raised.Adds
qiskit.quantum_info.random_clifford()for generating a randomqiskit.quantum_info.Cliffordoperator.Add
qiskit.quantum_info.random_quantum_channel()function for generating a random quantum channel with fixedChoi-rank in theStinespringrepresentation.Add
qiskit.quantum_info.random_hermitian()for generating a random HermitianOperator.Add
qiskit.quantum_info.random_statevector()for generating a randomStatevector.Adds
qiskit.quantum_info.random_pauli_table()for generating a randomqiskit.quantum_info.PauliTable.Adds
qiskit.quantum_info.random_stabilizer_table()for generating a randomqiskit.quantum_info.StabilizerTable.Add a
num_qubitsattribute toqiskit.quantum_info.StateVectorandqiskit.quantum_info.DensityMatrixclasses. This returns the number of qubits for N-qubit states and returnsNonefor non-qubit states.Adds
to_dict()andto_dict()methods to convertqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixobjects into Bra-Ket notation dictionary.Example
from qiskit.quantum_info import Statevector state = Statevector.from_label('+0') print(state.to_dict())
{'00': (0.7071067811865475+0j), '10': (0.7071067811865475+0j)}from qiskit.quantum_info import DensityMatrix state = DensityMatrix.from_label('+0') print(state.to_dict())
{'00|00': (0.4999999999999999+0j), '10|00': (0.4999999999999999+0j), '00|10': (0.4999999999999999+0j), '10|10': (0.4999999999999999+0j)}Adds
probabilities()andprobabilities()toqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixclasses which return an array of measurement outcome probabilities in the computational basis for the specified subsystems.Example
from qiskit.quantum_info import Statevector state = Statevector.from_label('+0') print(state.probabilities())
[0.5 0. 0.5 0. ]
from qiskit.quantum_info import DensityMatrix state = DensityMatrix.from_label('+0') print(state.probabilities())
[0.5 0. 0.5 0. ]
Adds
probabilities_dict()andprobabilities_dict()toqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixclasses which return a count-style dictionary array of measurement outcome probabilities in the computational basis for the specified subsystems.from qiskit.quantum_info import Statevector state = Statevector.from_label('+0') print(state.probabilities_dict())
{'00': 0.4999999999999999, '10': 0.4999999999999999}from qiskit.quantum_info import DensityMatrix state = DensityMatrix.from_label('+0') print(state.probabilities_dict())
{'00': 0.4999999999999999, '10': 0.4999999999999999}Add
sample_counts()andsample_memory()methods to theStatevectorandDensityMatrixclasses for sampling measurement outcomes on subsystems.Example:
Generate a counts dictionary by sampling from a statevector
from qiskit.quantum_info import Statevector psi = Statevector.from_label('+0') shots = 1024 # Sample counts dictionary counts = psi.sample_counts(shots) print('Measure both:', counts) # Qubit-0 counts0 = psi.sample_counts(shots, [0]) print('Measure Qubit-0:', counts0) # Qubit-1 counts1 = psi.sample_counts(shots, [1]) print('Measure Qubit-1:', counts1)
Measure both: {'00': 514, '10': 510} Measure Qubit-0: {'0': 1024} Measure Qubit-1: {'0': 497, '1': 527}Return the array of measurement outcomes for each sample
from qiskit.quantum_info import Statevector psi = Statevector.from_label('-1') shots = 10 # Sample memory mem = psi.sample_memory(shots) print('Measure both:', mem) # Qubit-0 mem0 = psi.sample_memory(shots, [0]) print('Measure Qubit-0:', mem0) # Qubit-1 mem1 = psi.sample_memory(shots, [1]) print('Measure Qubit-1:', mem1)
Measure both: ['11' '11' '11' '01' '01' '11' '11' '01' '01' '01'] Measure Qubit-0: ['1' '1' '1' '1' '1' '1' '1' '1' '1' '1'] Measure Qubit-1: ['0' '1' '1' '0' '1' '1' '0' '0' '1' '1']
Adds a
measure()method to theqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixquantum state classes. This allows sampling a single measurement outcome from the specified subsystems and collapsing the statevector to the post-measurement computational basis state. For examplefrom qiskit.quantum_info import Statevector psi = Statevector.from_label('+1') # Measure both qubits outcome, psi_meas = psi.measure() print("measure([0, 1]) outcome:", outcome, "Post-measurement state:") print(psi_meas) # Measure qubit-1 only outcome, psi_meas = psi.measure([1]) print("measure([1]) outcome:", outcome, "Post-measurement state:") print(psi_meas)
measure([0, 1]) outcome: 01 Post-measurement state: Statevector([0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j], dims=(2, 2)) measure([1]) outcome: 1 Post-measurement state: Statevector([0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j], dims=(2, 2))Adds a
reset()method to theqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixquantum state classes. This allows reseting some or all subsystems to the \(|0\rangle\) state. For examplefrom qiskit.quantum_info import Statevector psi = Statevector.from_label('+1') # Reset both qubits psi_reset = psi.reset() print("Post reset state: ") print(psi_reset) # Reset qubit-1 only psi_reset = psi.reset([1]) print("Post reset([1]) state: ") print(psi_reset)
Post reset state: Statevector([1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], dims=(2, 2)) Post reset([1]) state: Statevector([0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j], dims=(2, 2))A new visualization function
qiskit.visualization.visualize_transition()for visualizing single qubit gate transitions has been added. It takes in a single qubit circuit and returns an animation of qubit state transitions on a Bloch sphere. To use this function you must have installed the dependencies for and configured globally a matplotlib animtion writer. You can refer to the matplotlib documentation for more details on this. However, in the default case simply ensuring that FFmpeg is installed is sufficient to use this function.It supports circuits with the following gates:
HGateXGateYGateZGateRXGateRYGateRZGateSGateSdgGateTGateTdgGateU1Gate
For example:
from qiskit.visualization import visualize_transition from qiskit import * qc = QuantumCircuit(1) qc.h(0) qc.ry(70,0) qc.rx(90,0) qc.rz(120,0) visualize_transition(qc, fpg=20, spg=1, trace=True)