qiskit_superstaq

Submodules

Attributes

AQTiToffoliGate

Classes

`AQTiCCXGate`	A subclass of the iCCXGate for AQT where the control state is "00".
`AceCR`	Active Cancellation Echoed Cross Resonance gate, supporting polarity switches and sandwiches.
`DDGate`	The Dipole-Dipole (DD) gate, which performs the dipole-dipole interaction between two
`ParallelGates`	A single gate combining a collection of concurrent gate(s) acting on different qubits.
`StrippedCZGate`	The Stripped CZ gate is a regular CZ gate when the rz angle = 0.
`SuperstaqBackend`	This class represents a Superstaq backend.
`SuperstaqJob`	This class represents a Superstaq job instance.
`SuperstaqProvider`	Provider for Superstaq backend.
`ZZSwapGate`	The ZZ-SWAP gate, which performs the ZZ-interaction followed by a SWAP.

Functions

`active_qubit_indices`(→ list[int])	Returns the indices of the non-idle qubits in the input quantum circuit.
`classical_bit_mapping`(→ dict[int, int])	Returns the index of the (final) measured qubit associated with each classical bit.
`deserialize_circuits`(→ list[qiskit.QuantumCircuit])	Deserializes serialized qiskit.QuantumCircuit(s).
`measured_qubit_indices`(→ list[int])	Returns the indices of the measured qubits in the input quantum circuit.
`serialize_circuits`(→ str)	Serializes qiskit.QuantumCircuit(s) into a single string.

Package Contents

class qiskit_superstaq.AQTiCCXGate(label: str | None = None)

Bases: iCCXGate

A subclass of the iCCXGate for AQT where the control state is “00”.

class qiskit_superstaq.AceCR(rads: str | float = np.pi / 2, sandwich_rx_rads: float = 0, label: str | None = None)

Bases: qiskit.circuit.Gate

Active Cancellation Echoed Cross Resonance gate, supporting polarity switches and sandwiches.

The typical AceCR in the literature is a positive half-CR, then X on “Z side”, then negative half-CR (where “Z side” and “X side” refer to the two sides of the underlying ZX interactions).

inverse() → AceCR

Inverts the AceCR gate.

Returns:: The inverse AceCR gate.

class qiskit_superstaq.DDGate(theta: float, label: str | None = None)

Bases: qiskit.circuit.Gate

The Dipole-Dipole (DD) gate, which performs the dipole-dipole interaction between two electrons. Part of the EeroQ native gateset.

The unitary for a DD gate parametrized by angle \(\theta\) is:

\[\begin{split}\begin{bmatrix} e^{-i \theta} & . & . & . \\ . & e^{i \theta}cos{theta} ie^{i \theta}sin{theta} & . \\ . & ie^{i \theta}sin{theta} e^{i \theta}cos{theta} & . \\ . & . & . & e^{-i \theta} \\ \end{bmatrix}\end{split}\]

where ‘.’ means ‘0’.

inverse() → DDGate

Inverts the DD gate.

Returns:: The inverse DD gate.

class qiskit_superstaq.ParallelGates(*component_gates: qiskit.circuit.Gate, label: str | None = None)

Bases: qiskit.circuit.Gate

A single gate combining a collection of concurrent gate(s) acting on different qubits.

inverse() → ParallelGates

Inverts parallel gates.

Returns:: The inverse parallel gates.

component_gates: tuple[qiskit.circuit.Gate, Ellipsis] = ()

class qiskit_superstaq.StrippedCZGate(rz_rads: float, label: str | None = None)

Bases: qiskit.circuit.Gate

The Stripped CZ gate is a regular CZ gate when the rz angle = 0.

It is the gate that is actually being performed by Sqale, and it is corrected into a CZ gate by RZ gates afterwards if the rz angle is nonzero.

inverse() → StrippedCZGate

Inverts the stripped CZ gate.

Returns:: The inverse Stripped CZ gate.

class qiskit_superstaq.SuperstaqBackend(provider: qiskit_superstaq.SuperstaqProvider, target: str)

Bases: qiskit.providers.BackendV2

This class represents a Superstaq backend.

aqt_compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], *, num_eca_circuits: int | None = None, random_seed: int | None = None, atol: float | None = None, gate_defs: collections.abc.Mapping[str, str | numpy.typing.NDArray[numpy.number[Any]] | None] | None = None, gateset: collections.abc.Mapping[str, collections.abc.Sequence[collections.abc.Sequence[int]]] | None = None, pulses: object = None, variables: object = None, **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles and optimizes the given circuit(s) for the Advanced Quantum Testbed (AQT).

AQT is a superconducting transmon quantum computing testbed at Lawrence Berkeley National Laboratory. More information can be found at https://aqt.lbl.gov.

Specifying a nonzero value for num_eca_circuits enables compilation with Equivalent Circuit Averaging (ECA). See https://arxiv.org/abs/2111.04572 for a description of ECA.

Parameters:

circuits – The circuit(s) to compile.
num_eca_circuits – Optional number of logically equivalent random circuits to generate from each input circuit for Equivalent Circuit Averaging (ECA).
random_seed – Optional seed used for approximate synthesis and ECA.
atol – An optional tolerance to use for approximate gate synthesis.
gate_defs – An optional dictionary mapping names in qtrl configs to operations, where each operation can be either a unitary matrix or None. More specific associations take precedence, for example {“SWAP”: <matrix1>, “SWAP/C5C4”: <matrix2>} implies <matrix1> for all “SWAP” calibrations except “SWAP/C5C4” (which will instead be mapped to <matrix2> applied to qubits 4 and 5). Setting any calibration to None will disable that calibration.
gateset – Which gates to use for compilation. Should be a dictionary with entries in the for gate_name: [[1, 2], [3, 4], where the keys refer to specific gates, and the values indicate which qubit(s) they act upon.
pulses – Qtrl PulseManager or file path for pulse configuration.
variables – Qtrl VariableManager or file path for variable configuration.
kwargs – Other desired compile options.

Returns:

Object whose .circuit(s) attribute contains the optimized circuits(s). Alternatively for ECA, an Object whose .circuits attribute is a list (or list of lists) of logically equivalent circuits. If qtrl is installed, the object’s .seq attribute is a qtrl Sequence object containing pulse sequences for each compiled circuit.

Raises:

ValueError – If this is not an AQT backend.

compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles the given circuit(s) to the backend’s native gateset.

Parameters:

circuits – The qiskit.QuantumCircuit(s) to compile.
kwargs – Other desired compile options.

Returns:

A CompilerOutput object whose .circuit(s) attribute contains optimized compiled circuit(s).

Raises:

ValueError – If this backend does not support compilation.

configuration() → qiskit.providers.models.BackendConfiguration

Retrieves configuration information for this target.

Returns:: A backend configuration object containing various hardware parameters.

cq_compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], *, grid_shape: tuple[int, int] | None = None, control_radius: float = 1.0, stripped_cz_rads: float = 0.0, **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles and optimizes the given circuit(s) for CQ devices.

Parameters:

circuits – The qiskit.QuantumCircuit(s) to compile.
grid_shape – Optional fixed dimensions for the rectangular qubit grid (by default the actual qubit layout will be pulled from the hardware provider).
control_radius – The radius with which qubits remain connected (ie 1.0 indicates nearest neighbor connectivity).
stripped_cz_rads – The angle in radians of the stripped cz gate.
kwargs – Other desired compile options.

Returns:

A CQ CompilerOutput object.

Raises:

ValueError – If this is not a CQ backend.

ibmq_compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], *, dynamical_decoupling: bool = True, dd_strategy: str = 'adaptive', **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles and optimizes the given circuit(s) for IBMQ devices.

Superstaq currently supports the following dynamical decoupling strategies:

“standard”: Places a single DD sequence in each idle window.
“syncopated”: Places DD pulses at fixed time intervals, alternating between pulses on
neighboring qubits in order to mitigate parasitic ZZ coupling errors.
“adaptive” (default): Dynamically spaces DD pulses across idle windows with awareness of
neighboring qubits to achieve the parasitic ZZ coupling mitigation of the “syncopated” strategy with fewer pulses and less discretization error.

See https://superstaq.readthedocs.io/en/latest/optimizations/ibm/ibmq_dd_strategies_qiskit_superstaq.html for an example of each strategy.

Parameters:

circuits – The qiskit.QuantumCircuit(s) to compile.
dynamical_decoupling – Applies dynamical decoupling optimization to circuit(s).
dd_strategy – Method to use for placing dynamical decoupling operations; should be either “standard”, “syncopated”, or “adaptive” (default). See above.
kwargs – Other desired compile options.

Returns:

Object whose .circuit(s) attribute contains the compiled circuits(s), and whose .pulse_gate_circuit(s) attribute contains the corresponding pulse schedule(s) (when available).

Raises:

ValueError – If this is not an IBMQ backend.

process_aces(job_id: str) → list[float]

Process a job submitted through submit_aces.

Parameters:: job_id – The job id returned by submit_aces.
Returns:: The estimated eigenvalues.

qscout_compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], *, mirror_swaps: bool = False, base_entangling_gate: str = 'xx', num_qubits: int | None = None, error_rates: _typeshed.SupportsItems[tuple[int, Ellipsis], float] | None = None, **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles and optimizes the given circuit(s) for the QSCOUT trapped-ion testbed at Sandia National Laboratories [1].

Compiled circuits are returned as both qiskit.QuantumCircuit objects and corresponding Jaqal [2] programs (strings).

References

[1] S. M. Clark et al., Engineering the Quantum Scientific Computing Open User: Testbed, IEEE Transactions on Quantum Engineering Vol. 2, 3102832 (2021). https://doi.org/10.1109/TQE.2021.3096480.
[2] B. Morrison, et al., Just Another Quantum Assembly Language (Jaqal), 2020 IEEE: International Conference on Quantum Computing and Engineering (QCE), 402-408 (2020). https://arxiv.org/abs/2008.08042.

Parameters:

circuits – The circuit(s) to compile.
mirror_swaps – Whether to use mirror swapping to reduce two-qubit gate overhead.
base_entangling_gate – The base entangling gate to use (“xx”, “zz”, “sxx”, or “szz”). Compilation with the “xx” and “zz” entangling bases will use arbitrary parameterized two-qubit interactions, while the “sxx” and “szz” bases will only use fixed maximally-entangling rotations.
num_qubits – An optional number of qubits that should be present in the compiled circuit(s) and Jaqal program(s) (otherwise this will be determined from the input).
error_rates – Optional dictionary assigning relative error rates to pairs of physical qubits, in the form {<qubit_indices>: <error_rate>, …} where <qubit_indices> is a tuple physical qubit indices (ints) and <error_rate> is a relative error rate for gates acting on those qubits (for example {(0, 1): 0.3, (1, 2): 0.2}) . If provided, Superstaq will attempt to map the circuit to minimize the total error on each qubit.
kwargs – Other desired qscout_compile options.

Returns:

Object whose .circuit(s) attribute contains optimized qiskit.QuantumCircuit`(s), and `.jaqal_program(s) attribute contains the corresponding Jaqal program(s).

Raises:

ValueError – If this is not a QSCOUT backend.
ValueError – If base_entangling_gate is not a valid entangling basis.

resource_estimate(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit]) → general_superstaq.ResourceEstimate | list[general_superstaq.ResourceEstimate]

Generates resource estimates for qiskit circuit(s).

Parameters:: circuits – The circuit(s) used during resource estimation.
Returns:: ResourceEstimate(s) containing resource costs (after compilation) for running circuit(s) on this backend.

retrieve_job(job_id: str) → qiskit_superstaq.SuperstaqJob

Gets a job that has been created on the Superstaq API. :param job_id: The UUID of the job. Jobs are assigned these numbers by the server during the :param creation of the job.:

Returns:: A qiskit_superstaq.SuperstaqJob which can be queried for status or results.
Raises:: SuperstaqServerException – If there was an error accessing the API.

run(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], shots: int, method: str | None = None, **kwargs: Any) → qiskit_superstaq.SuperstaqJob

Runs circuits on the stored Superstaq backend.

Parameters:

circuits – A list of circuits to run.
shots – The number of execution shots (times to run the circuit).
method – An optional string that describes the execution method (e.g. ‘dry-run’, ‘statevector’, etc.).
kwargs – Other optimization and execution parameters.

Returns:

A Superstaq job storing ID and other related info.

Raises:

ValueError – If circuits contains invalid circuits for submission.

submit_aces(qubits: collections.abc.Sequence[int], shots: int, num_circuits: int, mirror_depth: int, extra_depth: int, method: str | None = None, noise: str | None = None, error_prob: float | tuple[float, float, float] | None = None, tag: str | None = None, lifespan: int | None = None, weights: collections.abc.Iterable[int] | None = None) → str

Submits the jobs to characterize this target through the ACES protocol.

The following gate eigenvalues are eestimated. For each qubit in the device, we consider six Clifford gates. These are given by the XZ maps: XZ, ZX, -YZ, -XY, ZY, YX. For each of these gates, three eigenvalues are returned (X, Y, Z, in that order). Then, the two-qubit gate considered here is the CZ in linear connectivity (each qubit n with n + 1). For this gate, 15 eigenvalues are considered: XX, XY, XZ, XI, YX, YY, YZ, YI, ZX, ZY, ZZ, ZI, IX, IY IZ, in that order.

If n qubits are characterized, the first 18 * n entries of the list returned by process_aces will contain the single-qubit eigenvalues for each gate in the order above. After all the single-qubit eigenvalues, the next 15 * (n - 1) entries will contain for the CZ connections, in ascending order.

The protocol in detail can be found in: https://arxiv.org/abs/2108.05803.

Parameters:

qubits – A list with the qubit indices to characterize.
shots – How many shots to use per circuit submitted.
num_circuits – How many random circuits to use in the protocol.
mirror_depth – The half-depth of the mirror portion of the random circuits.
extra_depth – The depth of the fully random portion of the random circuits.
method – Which type of method to execute the circuits with.
noise – Noise model to simulate the protocol with. Valid strings are “symmetric_depolarize”, “phase_flip”, “bit_flip” and “asymmetric_depolarize”.
error_prob – The error probabilities if a string was passed to noise. * For “asymmetric_depolarize”, error_prob will be a three-tuple with the error rates for the X, Y, Z gates in that order. So, a valid argument would be error_prob = (0.1, 0.1, 0.1). Notice that these values must add up to less than or equal to 1. * For the other channels, error_prob is one number less than or equal to 1, e.g., error_prob = 0.1.
tag – Tag for all jobs submitted for this protocol.
lifespan – How long to store the jobs submitted for in days (only works with right permissions).
weights – Valid Pauli string weights for probes.

Returns:

A string with the job id for the ACES job created.

Raises:

AssertionError – If the weights are not an Iterable type.
ValueError – If the target or noise model is not valid.
SuperstaqServerException – If the request fails.

target_info() → dict[str, Any]

Retrieves configuration information for this target.

Returns:: A dictionary containing various hardware parameters.

property coupling_map: qiskit.transpiler.CouplingMap | None: A coupling map generated from the two-qubit gates supported by this backend.

property max_circuits: int | None: The maximum number of circuits that can be submitted to this backend.

property target: qiskit.transpiler.Target: A qiskit.transpiler.Target object for this backend.

class qiskit_superstaq.SuperstaqJob(backend: qiskit_superstaq.SuperstaqBackend, job_id: str)

Bases: qiskit.providers.JobV1

This class represents a Superstaq job instance.

cancel(index: int | None = None, **kwargs: object) → None

Cancel the current job if it is not in a terminal state.

Parameters:

index – An optional index of the specific sub-job to cancel.
kwargs – Extra options needed to fetch jobs.

Raises:

SuperstaqServerException – If unable to get the status of the job from the API or cancellations were unsuccessful.

compiled_circuits(index: int) → qiskit.QuantumCircuit

compiled_circuits(index: None = None) → list[qiskit.QuantumCircuit]

Gets the compiled circuits that were processed for this job.

Parameters:: index – An optional index of the specific circuit to retrieve.
Returns:: A single compiled circuit or list of compiled circuits.

input_circuits(index: int) → qiskit.QuantumCircuit

input_circuits(index: None = None) → list[qiskit.QuantumCircuit]

Gets the original circuits that were submitted for this job.

Parameters:: index – An optional index of the specific circuit to retrieve.
Returns:: The input circuit or list of submitted input circuits.

pulse_gate_circuits(index: int) → qiskit.QuantumCircuit

pulse_gate_circuits(index: None = None) → list[qiskit.QuantumCircuit]

Gets the pulse gate circuit(s) returned by this job.

Parameters:: index – An optional index of the pulse gate circuit to retrieve.
Returns:: A single pulse gate circuit or list of pulse gate circuits.

result(index: int | None = None, timeout: float | None = None, wait: float = 5, qubit_indices: collections.abc.Sequence[int] | None = None) → qiskit.result.Result

Retrieves the result data associated with a Superstaq job.

Parameters:

index – An optional index to retrieve a specific result from a result list.
timeout – An optional parameter that fixes when result retrieval times out. Units are in seconds.
wait – An optional parameter that sets the interval to check for Superstaq job results. Units are in seconds. Defaults to 5.
qubit_indices – The qubit indices to return the results of individually.

Returns:

A qiskit result object containing job information.

status(index: int | None = None) → qiskit.providers.jobstatus.JobStatus

Query for the equivalent qiskit job status.

Parameters:: index – An optional index to retreive a specific job status.
Returns:: The equivalent qiskit.providers.jobstatus.JobStatus type.

abstract submit() → None

Unsupported submission call.

Raises:: NotImplementedError – If a job is submitted via SuperstaqJob.

to_dict() → dict[str, general_superstaq.typing.Job]

Refreshes and returns job information.

Note

The contents of this dictionary are not guaranteed to be consistent over time. Whenever possible, users should use the specific SuperstaqJob methods to retrieve the desired job information instead of relying on particular entries in the output of this method.

Returns:: A dictionary containing updated job information.

ALL_STATES = ('Done', 'Cancelled', 'Failed', 'Queued', 'Submitted', 'Running')

PROCESSING_STATES = ('Queued', 'Submitted', 'Running')

TERMINAL_STATES = ('Done', 'Cancelled', 'Failed')

class qiskit_superstaq.SuperstaqProvider(api_key: str | None = None, remote_host: str | None = None, api_version: str = gss.API_VERSION, max_retry_seconds: int = 3600, verbose: bool = False, cq_token: str | None = None, ibmq_token: str | None = None, ibmq_instance: str | None = None, ibmq_channel: str | None = None, **kwargs: Any)

Bases: general_superstaq.service.Service

Provider for Superstaq backend.

Typical usage is:

import qiskit_superstaq as qss

ss_provider = qiskit_superstaq.SuperstaqProvider('MY_TOKEN')

backend = ss_provider.get_backend('target')

where MY_TOKEN is the access token provided by Superstaq, and target is the name of the desired backend.

aqt_compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], target: str = 'aqt_keysight_qpu', *, num_eca_circuits: int | None = None, random_seed: int | None = None, atol: float | None = None, gate_defs: collections.abc.Mapping[str, str | numpy.typing.NDArray[numpy.number[Any]] | None] | None = None, gateset: collections.abc.Mapping[str, collections.abc.Sequence[collections.abc.Sequence[int]]] | None = None, pulses: object = None, variables: object = None, **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles and optimizes the given circuit(s) for the Advanced Quantum Testbed (AQT).

AQT is a superconducting transmon quantum computing testbed at Lawrence Berkeley National Laboratory. More information can be found at https://aqt.lbl.gov.

Specifying a nonzero value for num_eca_circuits enables compilation with Equivalent Circuit Averaging (ECA). See https://arxiv.org/abs/2111.04572 for a description of ECA.

Parameters:

circuits – The circuit(s) to compile.
target – A string containing the name of a target AQT backend.
num_eca_circuits – Optional number of logically equivalent random circuits to generate from each input circuit for Equivalent Circuit Averaging (ECA).
random_seed – Optional seed used for approximate synthesis and ECA.
atol – An optional tolerance to use for approximate gate synthesis.
gate_defs – An optional dictionary mapping names in qtrl configs to operations, where each operation can be either a unitary matrix or None. More specific associations take precedence, for example {“SWAP”: <matrix1>, “SWAP/C5C4”: <matrix2>} implies <matrix1> for all “SWAP” calibrations except “SWAP/C5C4” (which will instead be mapped to <matrix2> applied to qubits 4 and 5). Setting any calibration to None will disable that calibration.
gateset – Which gates to use for compilation. Should be a dictionary with entries in the for gate_name: [[1, 2], [3, 4], where the keys refer to specific gates, and the values indicate which qubit(s) they act upon.
pulses – Qtrl PulseManager or file path for pulse configuration.
variables – Qtrl VariableManager or file path for variable configuration.
kwargs – Other desired compile options.

Returns:

Object whose .circuit(s) attribute contains the optimized circuits(s). Alternatively for ECA, an Object whose .circuits attribute is a list (or list of lists) of logically equivalent circuits. If qtrl is installed, the object’s .seq attribute is a qtrl Sequence object containing pulse sequences for each compiled circuit.

Raises:

ValueError – If target is not a valid AQT target.

aqt_compile_eca(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], num_equivalent_circuits: int, random_seed: int | None = None, target: str = 'aqt_keysight_qpu', atol: float | None = None, gate_defs: collections.abc.Mapping[str, str | numpy.typing.NDArray[numpy.number[Any]] | None] | None = None, **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles and optimizes the given circuit(s) for the Advanced Quantum Testbed (AQT) at Lawrence Berkeley National Laboratory using Equivalent Circuit Averaging (ECA).

See arxiv.org/pdf/2111.04572.pdf for a description of ECA.

Note

This method has been deprecated. Instead, use the num_eca_circuits argument of aqt_compile().

Parameters:

circuits – The circuit(s) to compile.
num_equivalent_circuits – Number of logically equivalent random circuits to generate for each input circuit.
random_seed – Optional seed for circuit randomizer.
target – A string containing the name of a target AQT backend.
atol – An optional tolerance to use for approximate gate synthesis.
gate_defs – An optional dictionary mapping names in qtrl configs to operations, where each operation can be either a unitary matrix or None. More specific associations take precedence, for example {“SWAP”: <matrix1>, “SWAP/C5C4”: <matrix2>} implies <matrix1> for all “SWAP” calibrations except “SWAP/C5C4” (which will instead be mapped to <matrix2> applied to qubits 4 and 5). Setting any calibration to None will disable that calibration.
kwargs – Other desired aqt_compile_eca options.

Returns:

Object whose .circuits attribute is a list (or list of lists) of logically equivalent circuits. If qtrl is installed, the object’s .seq attribute is a qtrl Sequence object containing pulse sequences for each compiled circuit.

Raises:

ValueError – If target is not a valid AQT target.

Lists the backends available from this provider.

Parameters:

simulator – Optional flag to restrict the list of targets to (non-) simulators.
supports_submit – Optional boolean flag to only return targets that (don’t) allow circuit submissions.
supports_submit_qubo – Optional boolean flag to only return targets that (don’t) allow qubo submissions.
supports_compile – Optional boolean flag to return targets that (don’t) support circuit compilation.
available – Optional boolean flag to only return targets that are (not) available to use.
retired – Optional boolean flag to only return targets that are or are not retired.
accessible – Optional boolean flag to only return targets that are (not) accessible to the user.
kwargs – Any additional, supported flags to restrict/filter returned targets.

Returns:

A list of Superstaq backends.

cq_compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], target: str = 'cq_sqale_qpu', *, grid_shape: tuple[int, int] | None = None, control_radius: float = 1.0, stripped_cz_rads: float = 0.0, **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles the given circuit(s) to CQ device, optimized to its native gate set.

Parameters:

circuits – The circuit(s) to compile.
target – String of target CQ device.
grid_shape – Optional fixed dimensions for the rectangular qubit grid (by default the actual qubit layout will be pulled from the hardware provider).
control_radius – The radius with which qubits remain connected (ie 1.0 indicates nearest neighbor connectivity).
stripped_cz_rads – The angle in radians of the stripped cz gate.
kwargs – Other desired cq_compile options.

Returns:

object whose .circuit(s) attribute is an optimized qiskit circuit(s).

Raises:

ValueError – If target is not a valid CQ target.

get_backend(target: str) → qiskit_superstaq.SuperstaqBackend

Returns a Superstaq backend.

Parameters:: target – A string containing the name of a target backend.
Returns:: A Superstaq backend.

get_job(job_id: str) → qiskit_superstaq.SuperstaqJob

Gets a job that has been created on the Superstaq API.

Parameters:

job_id – The UUID of the job. Jobs are assigned these numbers by the server during the
job. (creation of the) –

Returns:

A qiskit_superstaq.SuperstaqJob which can be queried for status or results.

Raises:

SuperstaqServerException – If there was an error accessing the API.
SuperstaqException – If retrived jobs are from different targets.

ibmq_compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], target: str, *, dynamical_decoupling: bool = True, dd_strategy: str = 'adaptive', **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Returns pulse schedule(s) for the given qiskit circuit(s) and target.

Superstaq currently supports the following dynamical decoupling strategies:

“standard”: Places a single DD sequence in each idle window.
“syncopated”: Places DD pulses at fixed time intervals, alternating between pulses on
neighboring qubits in order to mitigate parasitic ZZ coupling errors.
“adaptive” (default): Dynamically spaces DD pulses across idle windows with awareness of
neighboring qubits to achieve the parasitic ZZ coupling mitigation of the “syncopated” strategy with fewer pulses and less discretization error.

See https://superstaq.readthedocs.io/en/latest/optimizations/ibm/ibmq_dd_strategies_qiskit_superstaq.html for an example of each strategy.

Parameters:

circuits – The circuit(s) to compile.
target – A string containing the name of a target IBMQ backend.
dynamical_decoupling – Applies dynamical decoupling optimization to circuit(s).
dd_strategy – Method to use for placing dynamical decoupling operations; should be either “standard”, “syncopated”, or “adaptive” (default). See above.
kwargs – Other desired compile options.

Returns:

Object whose .circuit(s) attribute contains the compiled circuits(s), and whose .pulse_gate_circuit(s) attribute contains the corresponding pulse schedule(s) (when available).

Raises:

ValueError – If target is not a valid IBMQ target.

process_dfe(ids: list[str]) → float

Process the results of a DFE protocol.

Parameters:

ids – A list (size two) of ids returned by a call to submit_dfe.

Returns:

The estimated fidelity between the two states as a float.

Raises:

ValueError – If ids is not of size two.
SuperstaqServerException – If there was an error accessing the API or the jobs submitted through submit_dfe have not finished running.

qscout_compile(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], target: str = 'qscout_peregrine_qpu', *, mirror_swaps: bool = False, base_entangling_gate: str = 'xx', num_qubits: int | None = None, error_rates: _typeshed.SupportsItems[tuple[int, Ellipsis], float] | None = None, **kwargs: Any) → qiskit_superstaq.compiler_output.CompilerOutput

Compiles and optimizes the given circuit(s) for the QSCOUT trapped-ion testbed at Sandia National Laboratories [1].

Compiled circuits are returned as both qiskit.QuantumCircuit objects and corresponding Jaqal [2] programs (strings).

References

[1] S. M. Clark et al., Engineering the Quantum Scientific Computing Open User: Testbed, IEEE Transactions on Quantum Engineering Vol. 2, 3102832 (2021). https://doi.org/10.1109/TQE.2021.3096480.
[2] B. Morrison, et al., Just Another Quantum Assembly Language (Jaqal), 2020 IEEE: International Conference on Quantum Computing and Engineering (QCE), 402-408 (2020). https://arxiv.org/abs/2008.08042.

Parameters:

circuits – The circuit(s) to compile.
target – A string containing the name of a target backend.
mirror_swaps – Whether to use mirror swapping to reduce two-qubit gate overhead.
base_entangling_gate – The base entangling gate to use (“xx”, “zz”, “sxx”, or “szz”). Compilation with the “xx” and “zz” entangling bases will use arbitrary parameterized two-qubit interactions, while the “sxx” and “szz” bases will only use fixed maximally-entangling rotations.
num_qubits – An optional number of qubits that should be present in the compiled circuit(s) and Jaqal program(s) (otherwise this will be determined from the input).
error_rates – Optional dictionary assigning relative error rates to pairs of physical qubits, in the form {<qubit_indices>: <error_rate>, …} where <qubit_indices> is a tuple physical qubit indices (ints) and <error_rate> is a relative error rate for gates acting on those qubits (for example {(0, 1): 0.3, (1, 2): 0.2}). If provided, Superstaq will attempt to map the circuit to minimize the total error on each qubit.
kwargs – Other desired qscout_compile options.

Returns:

Object whose .circuit(s) attribute contains optimized qiskit.QuantumCircuit`(s), and `.jaqal_program(s) attribute contains the corresponding Jaqal program(s).

Raises:

ValueError – If target is not a valid QSCOUT target.
ValueError – If base_entangling_gate is not a valid gate option.

resource_estimate(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit], target: str) → general_superstaq.ResourceEstimate | list[general_superstaq.ResourceEstimate]

Generates resource estimates for qiskit circuit(s).

Parameters:

circuits – The circuit(s) used during resource estimation.
target – A string containing the name of a target backend.

Returns:

ResourceEstimate(s) containing resource costs (after compilation) for running circuit(s) on a backend.

submit_dfe(rho_1: tuple[qiskit.QuantumCircuit, str], rho_2: tuple[qiskit.QuantumCircuit, str], num_random_bases: int, shots: int, **kwargs: Any) → list[str]

Executes the circuits necessary for the DFE protocol.

The circuits used to prepare the desired states should not contain final measurements, but can contain mid-circuit measurements (as long as the intended target supports them). For example, to prepare a Bell state to be ran in ss_unconstrained_simulator, you should pass qc = qiskit.QuantumCircuit(2); qc.h(0); qc.cx(0, 1) as the first element of some rho_i (note there are no final measurements).

The fidelity between states is calculated following the random measurement protocol outlined in [1].

References

[1] Elben, Andreas, Benoît Vermersch, Rick van Bijnen, Christian Kokail, Tiff Brydges,: Christine Maier, Manoj K. Joshi, Rainer Blatt, Christian F. Roos, and Peter Zoller. “Cross-platform verification of intermediate scale quantum devices.” Physical review letters 124, no. 1 (2020): 010504.

Parameters:

rho_1 – Tuple containing the information to prepare the first state. It contains a qiskit.QuantumCircuit at index 0 and a target name at index 1.
rho_2 – Tuple containing the information to prepare the second state. It contains a qiskit.QuantumCircuit at index 0 and a target name at index 1.
num_random_bases – Number of random bases to measure each state in.
shots – Number of shots to use per random basis.
kwargs – Other execution parameters. - tag: Tag for all jobs submitted for this protocol. - lifespan: How long to store the jobs submitted for in days (only works with right permissions). - method: Which type of method to execute the circuits with.

Returns:

A list with two strings, which are the job ids that need to be passed to process_dfe to post-process the measurement results and return the fidelity.

Raises:

ValueError – If circuit is not a valid qiskit.QuantumCircuit.
SuperstaqServerException – If there was an error accessing the API.

supercheq(files: list[list[int]], num_qubits: int, depth: int) → tuple[list[qiskit.QuantumCircuit], numpy.typing.NDArray[numpy.float64]]

Returns Supercheq randomly generated circuits and the corresponding fidelity matrices.

References

[1] P. Gokhale et al., SupercheQ: Quantum Advantage for Distributed Databases, (2022).: https://arxiv.org/abs/2212.03850.

Parameters:

files – A list of files specified as binary using ints. For example: [[1, 0, 1], [1, 1, 1]].
num_qubits – The number of qubits to run Supercheq on.
depth – The depth of the circuits to run Supercheq on.

Returns:

A tuple containing a list of qiskit.QuantumCircuits and a list of corresponding fidelity matrices.

class qiskit_superstaq.ZZSwapGate(theta: float, label: str | None = None)

Bases: qiskit.circuit.Gate

The ZZ-SWAP gate, which performs the ZZ-interaction followed by a SWAP.

ZZ-SWAPs are useful for applications like QAOA or Hamiltonian Simulation, particularly on linear- or low- connectivity devices. See https://arxiv.org/pdf/2004.14970.pdf for an application of ZZ-SWAP networks.

The unitary for a ZZ-SWAP gate parametrized by ZZ-interaction angle \(\theta\) is:

\[\begin{split}\begin{bmatrix} 1 & . & . & . \\ . & . & e^{i \theta} & . \\ . & e^{i \theta} & . & . \\ . & . & . & 1 \\ \end{bmatrix}\end{split}\]

where ‘.’ means ‘0’. For \(\theta = 0\), the ZZ-SWAP gate is just an ordinary SWAP.

inverse() → ZZSwapGate

Inverts the ZZ-SWAP gate.

Returns:: The inverse ZZ-SWAP gate.

qiskit_superstaq.active_qubit_indices(circuit: qiskit.QuantumCircuit) → list[int]

Returns the indices of the non-idle qubits in the input quantum circuit.

Parameters:: circuit – A qiskit.QuantumCircuit circuit.
Returns:: A list containing the indices of the non-idle qubits.

qiskit_superstaq.classical_bit_mapping(circuit: qiskit.QuantumCircuit) → dict[int, int]

Returns the index of the (final) measured qubit associated with each classical bit.

If more than one measurement is assigned to the same classical bit, only the final measurement is considered.

Parameters:: circuit – A qiskit.QuantumCircuit circuit.
Returns:: A dictionary mapping classical bit indices to the indices of the measured qubits.

qiskit_superstaq.deserialize_circuits(serialized_circuits: str) → list[qiskit.QuantumCircuit]

Deserializes serialized qiskit.QuantumCircuit(s).

Parameters:: serialized_circuits – String generated via qiskit_superstaq.serialization.serialize_circuit().
Returns:: A list containing the deserialized circuits.
Raises:: ValueError – If serialized_circuits can’t be deserialized.

qiskit_superstaq.measured_qubit_indices(circuit: qiskit.QuantumCircuit) → list[int]

Returns the indices of the measured qubits in the input quantum circuit.

Parameters:: circuit – A qiskit.QuantumCircuit circuit.
Returns:: A list containing the indices of the measured qubits.

qiskit_superstaq.serialize_circuits(circuits: qiskit.QuantumCircuit | collections.abc.Sequence[qiskit.QuantumCircuit]) → str

Serializes qiskit.QuantumCircuit(s) into a single string.

Parameters:: circuits – A qiskit.QuantumCircuit or list of qiskit.QuantumCircuit to be serialized.
Returns:: A string representing the serialized circuit(s).

qiskit_superstaq.AQTiToffoliGate