laboneq_applications.contrib.experiments.single_qubit_randomized_benchmarking

This module defines a randomized benchmarking experiment.

In this experiment, ...

The experiment has the following pulse sequence:

qb --- [clifford sequence] --- [recovery gate] --- [ measure ]

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

Note that in the current implementation, the same Clifford sequences are applied on all qubits.

create_experiment(qpu, qubits, qasm_rb_sequences, gate_map, options=None)

Creates an Amplitude Rabi Experiment.

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
qasm_rb_sequences	list	RB sequences as QASM experiments.	required
gate_map	dict	Dictionary to define the native gate set in QASM and the corresponding quantum_operations's in LabOne Q. Default: {"id":None, "sx":"x90", "x":"x180", "rz":"rz"}.	required
options	TuneupExperimentOptions | 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
experiment	Experiment	The generated LabOne Q experiment instance to be compiled and executed.

Raises:

Type	Description
ValueError

Example

options = TuneupExperimentOptions()
options.count = 10
setup = DeviceSetup()
qpu = QPU(
    qubits=[TunableTransmonQubit("q0"), TunableTransmonQubit("q1")],
    quantum_operations=TunableTransmonOperations(),
)
temp_qubits = qpu.copy_qubits()
create_experiment(
    qpu=qpu,
    qubits=temp_qubits,
    length_cliffords=[1,5,10,20,50],
    variations=5,
    options=options,
)

create_sq_rb_qasm(length_cliffords, gate_map, variations=1, seed=None, options=None)

Creates RB sequences as QASM experiments.

Parameters:

Name	Type	Description	Default
length_cliffords	list	A list of RB sequences lengths.	required
gate_map	dict	Dictionary to define the native gate set in QASM and the corresponding quantum operations in LabOne Q.	required
variations	int	Number of samples to generate for each sequence length.	1
seed	int | None	A seed used to initialize numpy.random.default_rng when generating circuits.	None
options	TuneupExperimentOptions | 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
experiment	list	The generated LabOne Q experiment instance to be compiled and executed.

experiment_workflow(session, qpu, qubits, length_cliffords, variations=1, seed=None, gate_map=None, options=None)

The Randmized Benchmarking Workflow for single qubits.

The workflow consists of the following steps:

• create_experiment
• compile_experiment
• run_experiment

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
length_cliffords	list	list of numbers of Clifford gates to sweep	required
variations	int	Number of random seeds for RB.	1
seed	int | None	A seed used to initialize numpy.random.default_rng when generating circuits. Default is None and provides a random seed.	None
gate_map	dict | None	Dictionary to define the native gate set in QASM and the corresponding quantum_operations's in LabOne Q. Default: {"id":None, "sx":"x90", "x":"x180", "rz":"rz"}.	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
result	None	The result of the workflow.

Example

options = experiment_workflow.options()
options.count(10)
options.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,
    length_cliffords=[1,5,10,20,50],
    variations=5,
    options=options,
).run()