Primitives

QMEstimatorV2

qiskit_qm_provider.primitives.qm_estimator.QMEstimatorV2

A custom implementation of BaseEstimatorV2 optimized for QOP.

Generated QUA program (debugging)

QMEstimatorV2 automatically generates the underlying QUA program required to run your pubs on QOP. If you need to inspect what was generated, the returned job exposes the QUA Program on job.program, and you can print it as a QUA script via:

from qm import generate_qua_script
print(generate_qua_script(job.program))

See the full end-to-end snippet in Workflows and Examples.

__init__(backend: QMBackend, options: QMEstimatorOptions | dict | None = None)

  • backend: The QMBackend.

  • options: Options for the estimator.

run(pubs: Iterable[EstimatorPubLike], *, precision: float | None = None)

Runs the estimator.

QMEstimatorOptions

  • default_precision: Default precision (1/sqrt(shots)).

  • abelian_grouping: Group commuting observables (Default: True).

  • input_type: Mechanism for parameter loading (InputType.INPUT_STREAM, DGX_Q, etc.).

  • run_options: Dictionary of options passed to backend.run.

QMSamplerV2

qiskit_qm_provider.primitives.qm_sampler.QMSamplerV2

A custom implementation of BaseSamplerV2.

Generated QUA program (debugging)

QMSamplerV2 automatically generates the underlying QUA program required to run your pubs on QOP. If you need to inspect what was generated, the returned job exposes the QUA Program on job.program, and you can print it as a QUA script via:

from qm import generate_qua_script
print(generate_qua_script(job.program))

See the full end-to-end snippet in Workflows and Examples.

__init__(backend: QMBackend, options: QMSamplerOptions | dict | None = None)

  • backend: The QMBackend.

  • options: Options for the sampler.

run(pubs: Iterable[SamplerPubLike], *, shots: int | None = None)

Runs the sampler.

QMSamplerOptions

  • default_shots: Default number of shots (Default: 1024).

  • input_type: Mechanism for parameter loading.

  • run_options: Dictionary of options passed to backend.run.

  • meas_level: “classified”, “kerneled”, or “avg_kerneled”.