qiskit_superstaq.superstaq_backend

Classes

SuperstaqBackend

This class represents a Superstaq backend.

Module Contents

class qiskit_superstaq.superstaq_backend.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.

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.

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.

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.