qiskit_noise_learning.protocols.prepare_learning_program

qiskit_noise_learning.protocols.prepare_learning_program(backend: BackendV2, instructions: Sequence[CircuitInstruction], num_randomizations: int = 32, shots_per_randomization: int = 128, fragment_depths: Sequence[int] = (0, 1, 2, 4, 16, 32), creg_prefix: str = 'meas', local_clifford_ref_prefix: str = 'c', pass_manager: PassManager | None = None) → QuantumProgram

Build the quantum program of a standard noise learning experiment.

The experiment is setup to learn a 2-local Pauli-Lindblad model for each layer defined in instructions via the “vanilla” learning protocol, with paths generated via EvenDepthVanillaPaths and instruction sequences generated via VanillaInstructionSequences, as well as a SPAM noise model with perfect preparation and a 1-local measurement noise model.

Each instruction becomes one gate of the model. A box carrying a noise injection annotation takes that annotation’s reference as its gate name, and any other box is named automatically. The learned model comes back keyed by those names, so annotate the boxes to be identified in the result.

If an all-measurement box is supplied in instructions (i.e. one that measures every qubit appearing in all supplied instructions), that will be treated as the measurement layer. If no such layer is supplied, a default one will be built with name "M".

Example:

circuit = QuantumCircuit(backend.num_qubits)
with circuit.box([Twirl(), InjectNoise("layer")]):
    circuit.cz(17, 27)

program = prepare_learning_program(backend, [circuit[0]])

Parameters

• backend – The backend supplying the compilation target: the gate set, coupling map and qubit count that generated circuits are built against.
• instructions – The instructions to learn the noise of. Each instruction should contain a BoxOp operation, and must be self-inverse, with the exception of a box that measures all relevant qubits.
• num_randomizations – The number of randomizations to use per learning circuit.
• shots_per_randomization – The number of shots to use per randomization.
• fragment_depths – The fragment depths to use, that is, the number of repetitions of each path’s repeatable fragment.
• creg_prefix – The prefix assigned to all creg names used in learning experiment measurements. Defaults to "meas".
• local_clifford_ref_prefix – The prefix assigned to all local Clifford parameter references in template circuits. Defaults to "c".
• pass_manager – An optional pass manager to apply to every template circuit generated.

Returns

The program to submit, whose result process_learning_result() consumes.

Raises

ValueError – If instructions is empty, if any instruction does not contain a BoxOp, if num_randomizations or shots_per_randomization is less than one, if any entry of fragment_depths is negative, if more than one instruction measures, if a measuring instruction also contains other operations or does not measure every qubit that instructions act on, if backend.target does not support an operation of one of the boxes, or if a classical register added by pass_manager is not measured into exactly once.