FineAmplitudeCal#

class FineAmplitudeCal(physical_qubits, calibrations, schedule_name, backend=None, cal_parameter_name='amp', auto_update=True, gate=None, measurement_qubits=None)[source]#

A calibration version of the FineAmplitude experiment.

Overview

FineAmplitudeCal is a subclass of FineAmplitude. In the calibration experiment the circuits that are run have a custom gate with the pulse schedule attached to it through the calibrations.

Analysis class reference

FineAmplitudeAnalysis

Experiment options

These options can be set by the set_experiment_options() method.

Options
  • Defined in the class FineAmplitudeCal:

    • target_angle (float)

      Default value: 3.141592653589793
      The target angle of the pulse.
  • Defined in the class BaseCalibrationExperiment:

    • result_index (int)

      Default value: -1
      The index of the result from which to update the calibrations.
    • group (str)

      Default value: "default"
      The calibration group to which the parameter belongs. This will default to the value “default”.
  • Defined in the class FineAmplitude:

    • repetitions (List[int])

      Default value: [1, 2, 3, 4, 5, …]
      A list of the number of times that the gate is repeated.
    • gate (Gate)

      Default value: None
      This is a gate class such as XGate, so that one can obtain a gate by doing options.gate().
    • normalization (bool)

      Default value: True
      If set to True the DataProcessor will normalized the measured signal to the interval [0, 1]. Defaults to True.
    • add_cal_circuits (bool)

      Default value: True
      If set to True then two circuits to calibrate 0 and 1 points will be added. These circuits are often needed to properly calibrate the amplitude of the ping-pong oscillation that encodes the errors. This helps account for state preparation and measurement errors.
  • Defined in the class BaseExperiment:

    • max_circuits (Optional[int])

      Default value: None
      The maximum number of circuits per job when running an experiment on a backend.

See also

Initialization

See class FineAmplitude for details.

Parameters:
  • physical_qubits (Sequence[int]) – Sequence containing the qubit(s) for which to run the fine amplitude calibration. This can be a pair of qubits which correspond to control and target qubit.

  • calibrations (Calibrations) – The calibrations instance with the schedules.

  • schedule_name (str) – The name of the schedule to calibrate.

  • backend (Backend | None) – Optional, the backend to run the experiment on.

  • cal_parameter_name (str | None) – The name of the parameter in the schedule to update.

  • auto_update (bool) – Whether or not to automatically update the calibrations. By default this variable is set to True.

  • gate (Gate | None) – The gate to repeat in the quantum circuit. If this argument is None (the default), then the gate is built from the schedule name.

  • measurement_qubits (Sequence[int]) – The qubits in the given physical qubits that need to be measured.

Attributes

analysis: BaseAnalysis#

Return the analysis instance for the experiment.

Note

Analysis instance set to calibration experiment is implicitly patched to run calibration updater to update the parameters in the calibration table.

backend#

Return the backend for the experiment

calibrations#

Return the calibrations.

experiment_options#

Return the options for the experiment.

experiment_type#

Return experiment type.

num_qubits#

Return the number of qubits for the experiment.

physical_qubits#

Return the device qubits for the experiment.

run_options#

Return options values for the experiment run() method.

transpile_options#

Return the transpiler options for the run() method.

Methods

circuits()#

Create the circuits for the fine amplitude calibration experiment.

Returns:

A list of circuits with a variable number of gates.

Raises:

CalibrationError – If the analysis options do not contain the angle_per_gate.

Return type:

List[QuantumCircuit]

config()#

Return the config dataclass for this experiment

Return type:

ExperimentConfig

copy()#

Return a copy of the experiment

Return type:

BaseExperiment

enable_restless(rep_delay=None, override_processor_by_restless=True, suppress_t1_error=False)#

Enables a restless experiment by setting the restless run options and the restless data processor.

Parameters:
  • rep_delay (float | None) – The repetition delay. This is the delay between a measurement and the subsequent quantum circuit. Since the backends have dynamic repetition rates, the repetition delay can be set to a small value which is required for restless experiments. Typical values are 1 us or less.

  • override_processor_by_restless (bool) – If False, a data processor that is specified in the analysis options of the experiment is not overridden by the restless data processor. The default is True.

  • suppress_t1_error (bool) – If True, the default is False, then no error will be raised when rep_delay is larger than the T1 times of the qubits. Instead, a warning will be logged as restless measurements may have a large amount of noise.

Raises:
  • DataProcessorError – If the attribute rep_delay_range is not defined for the backend.

  • DataProcessorError – If a data processor has already been set but override_processor_by_restless is True.

  • DataProcessorError – If the experiment analysis does not have the data_processor option.

  • DataProcessorError – If the rep_delay is equal to or greater than the T1 time of one of the physical qubits in the experiment and the flag ignore_t1_check is False.

classmethod from_config(config)#

Initialize an experiment from experiment config

Return type:

BaseExperiment

job_info(backend=None)#

Get information about job distribution for the experiment on a specific backend.

Parameters:

backend (Backend) – Optional, the backend for which to get job distribution information. If not specified, the experiment must already have a set backend.

Returns:

A dictionary containing information about job distribution.

  • ”Total number of circuits in the experiment”: Total number of circuits in the experiment.

  • ”Maximum number of circuits per job”: Maximum number of circuits in one job based on backend and experiment settings.

  • ”Total number of jobs”: Number of jobs needed to run this experiment on the currently set backend.

Return type:

dict

Raises:

QiskitError – if backend is not specified.

run(backend=None, analysis='default', timeout=None, **run_options)#

Run an experiment and perform analysis.

Parameters:
  • backend (Backend | None) – Optional, the backend to run the experiment on. This will override any currently set backends for the single execution.

  • analysis (BaseAnalysis | None) – Optional, a custom analysis instance to use for performing analysis. If None analysis will not be run. If "default" the experiments analysis() instance will be used if it contains one.

  • timeout (float | None) – Time to wait for experiment jobs to finish running before cancelling.

  • run_options – backend runtime options used for circuit execution.

Returns:

The experiment data object.

Raises:

QiskitError – If experiment is run with an incompatible existing ExperimentData container.

Return type:

ExperimentData

set_experiment_options(**fields)#

Set the experiment options.

Parameters:

fields – The fields to update the options

Raises:

AttributeError – If the field passed in is not a supported options

set_run_options(**fields)#

Set options values for the experiment run() method.

Parameters:

fields – The fields to update the options

See also

The Setting options for your experiment guide for code example.

set_transpile_options(**fields)#

Add a warning message.

Note

If your experiment has overridden _transpiled_circuits and needs transpile options then please also override set_transpile_options.

update_calibrations(experiment_data)[source]#

Update the amplitude of the pulse in the calibrations.

The update rule of this experiment is

\[A \to A \frac{\theta_\text{target}}{\theta_\text{target} + {\rm d}\theta}\]

Where \(A\) is the amplitude of the pulse before the update.

Parameters:

experiment_data (ExperimentData) – The experiment data from which to extract the measured over/under rotation used to adjust the amplitude.