laboneq_applications.experiments.amplitude_rabi

This module defines the amplitude-rabi experiment.

In this experiment, we sweep the amplitude of a drive pulse on a given qubit transition in order to determine the pulse amplitude that induces a rotation of pi.

The amplitude-rabi experiment has the following pulse sequence:

qb --- [ prep transition ] --- [ x180_transition ] --- [ measure ]

If multiple qubits are passed to the run workflow, the above pulses are applied in parallel on all the qubits.

create_experiment(qpu, qubits, amplitudes, options=None)

Creates an Amplitude Rabi experiment Workflow.

Parameters:

Name	Type	Description	Default
qpu	QPU	The qpu consisting of the original qubits and quantum operations.	required
qubits	Qubits	The qubits to run the experiments on. May be either a single qubit or a list of qubits.	required
amplitudes	QubitSweepPoints	The amplitudes to sweep over for each qubit. If qubits is a single qubit, amplitudes must be a list of numbers or an array. Otherwise it must be a list of lists of numbers or arrays.	required
options	TuneupExperimentOptions | None	The options for building the experiment. See [TuneupExperimentOptions] and [BaseExperimentOptions] for accepted options. Overwrites the options from [TuneupExperimentOptions] and [BaseExperimentOptions].	None

Returns:

Name	Type	Description
experiment	Experiment	The generated LabOne Q experiment instance to be compiled and executed.

Raises:

Type	Description
ValueError	If the qubits and qubit_amplitudes are not of the same length.
ValueError	If qubit_amplitudes is not a list of numbers when a single qubit is passed.
ValueError	If qubit_amplitudes is not a list of lists of numbers.
ValueError	If the experiment uses calibration traces and the averaging mode is sequential.

Example

options = {
    "count": 10,
    "transition": "ge",
    "averaging_mode": "cyclic",
    "acquisition_type": "integration_trigger",
    "cal_traces": True,
}
options = TuneupExperimentOptions(**options)
qpu = QPU(
    qubits=[TunableTransmonQubit("q0"), TunableTransmonQubit("q1")],
    quantum_operations=TunableTransmonOperations(),
)
temp_qubits = qpu.copy_qubits()
create_experiment(
    qpu=qpu,
    qubits=temp_qubits,
    amplitudes=[
        np.linspace(0, 1, 11),
        np.linspace(0, 0.75, 11),
    ],
    options=options,
)

experiment_workflow(session, qpu, qubits, amplitudes, temporary_parameters=None, options=None)

The Amplitude Rabi Workflow.

The workflow consists of the following steps:

• create_experiment
• compile_experiment
• run_experiment
• analysis_workflow
• update_qubits

Parameters:

Name	Type	Description	Default
session	Session	The connected session to use for running the experiment.	required
qpu	QPU	The qpu consisting of the original qubits and quantum operations.	required
qubits	Qubits	The qubits to run the experiments on. May be either a single qubit or a list of qubits.	required
amplitudes	QubitSweepPoints	The amplitudes to sweep over for each qubit. If qubits is a single qubit, amplitudes must be a list of numbers or an array. Otherwise it must be a list of lists of numbers or arrays.	required
temporary_parameters	dict[str, dict | TransmonParameters] | None	The temporary parameters to update the qubits with.	None
options	TuneUpWorkflowOptions | None	The options for building the workflow. In addition to options from [WorkflowOptions], the following custom options are supported: - create_experiment: The options for creating the experiment.	None

Returns:

Name	Type	Description
WorkflowBuilder	None	The builder of the experiment workflow.

Example

options = TuneUpExperimentWorkflowOptions()
options.create_experiment.count = 10
options.create_experiment.transition = "ge"
qpu = QPU(
    qubits=[TunableTransmonQubit("q0"), TunableTransmonQubit("q1")],
    quantum_operations=TunableTransmonOperations(),
)
temp_qubits = qpu.copy_qubits()
result = experiment_workflow(
    session=session,
    qpu=qpu,
    qubits=temp_qubits,
    amplitudes=[
        np.linspace(0, 1, 11),
        np.linspace(0, 0.75, 11),
    ],
    options=options,
).run()