deltakit.explorer.Client.decode_measurements

Client.decode_measurements(measurements: Measurements, decoder: Decoder, ideal_stim_circuit: str | Circuit, noise_model: PhysicalNoiseModel, leakage_flags: LeakageFlags | None = None, sweep_bits: BinaryDataType | None = None) → DecodingResult

Accept raw measurement file and a clean stim circuit and return decoding results with a requested decoder.

Parameters:

• measurements (Measurements) – Measurements object.

• decoder (Decoder) –

  Predefined decoder object.

  • MWPM for Minimum-Weight Perfect Matching (https://arxiv.org/abs/2303.15933),

  • CC for Collision Clustering (https://arxiv.org/abs/2309.05558), or

  • BELIEF_MATCHING for Belief Matching (https://arxiv.org/abs/2203.04948).

  • BP_OSD for Belief Propagation - Ordered Statistics Decoding (BP-OSD) (https://quantum-journal.org/papers/q-2021-11-22-585/),

  • AC for Ambiguity Clustering (https://arxiv.org/abs/2406.14527).

  • LCD for Local Clustering Decoder (https://arxiv.org/abs/2411.10343).

• ideal_stim_circuit – (str | stim.Circuit): clean stim circuit.

• noise_model (PhysicalNoiseModel) – Noise model used to generate a circuit, or a noise model close to hardware, which has generated the measurements data.

• leakage_flags (Optional[LeakageFlags]) – Heralded leakage events, if available.

• sweep_bits (Optional[BinaryDataType]) – Initialisation bits, if were used in experiments.

Returns:

server response.

Return type:

DecodingResult

Examples

Decoding using experimental graph method:

decoder = Decoder(
    decoder_type=DecoderType.MWPM, use_experimental_graph=True
)
client.decode_measurements(
    measurements=measurements,
    decoder=decoder,
    ideal_stim_circuit=circuit,
    noise_model=PhysicalNoiseModel.get_floor_superconducting_noise(),
    sweep_bits=sweep_bits,
)

Decoding with AC with additional parameters:

decoder = types.Decoder(
    decoder_type=types.DecoderType.AC,
    parallel_jobs=8,
    parameters={
        "decompose_errors": False,
        "bp_rounds": 99,
        "ac_kappa_proportion": 0.02,
    },
)
client.decode_measurements(
    measurements=measurements,
    decoder=decoder,
    ideal_stim_circuit=compiled_circuit,
    noise_model=noise_model,
)