topqad_sdk.noiseprofiler.qre_noiseprofile package

Create the noise profile needed to perform qre.

topqad_sdk.noiseprofiler.qre_noiseprofile.get_preset_names()

Get the available preset names.

topqad_sdk.noiseprofiler.qre_noiseprofile.noise_profile_from_preset(preset_name: str)

Load a precomputed noise profile appropriate for quantum resource estimation.

Parameters:

preset_name (str) – The name of the noise profile to load.

Options include:

• “physical_depolarizing_baseline”

• “physical_depolarizing_baseline_throughput_matched”

• “physical_depolarizing_baseline_logical_time_matched”

• “physical_depolarizing_target”

• “physical_depolarizing_target_throughput_matched”

• “physical_depolarizing_target_logical_time_matched”

• “physical_depolarizing_desired”

• “physical_depolarizing_desired_throughput_matched”

• “physical_depolarizing_desired_logical_time_matched”

The profiles have the noise model parameters as follows:

• physical_depolarizing_baseline*: parameters associated with existing state-of-the-art hardware.

• physical_depolarizing_target*: parameters aligned with near-term research goals.

• physical_depolarizing_desired*: an optimistic target for future high-quality devices.

The profiles have decoder reaction times as follows:

• base: a SotA2025 decoder that has high reaction time compared to the logical cycle time.

• throughput-matched: the decoder’s throughput matches the logical cycle time. However, the reaction time of the decoder is larger than the logical cycle time.

• logical_time_matched: reaction time equal to the logical cycle time.

Raises:

ValueError – If unknown preset name provided.

Returns:

A json string with the noise profile.

Return type:

str

topqad_sdk.noiseprofiler.qre_noiseprofile.noise_profile_from_protocols(memory: Memory, magic_state_prep: MagicStatePreparationHookInjection | MagicStatePreparationRepCode, lattice_surgery_distance: LatticeSurgery, lattice_surgery_rounds: LatticeSurgery)

Create a noise profile from a set of already computed protocols.

This noise profile can be passed to “qre” to obtain a resource estimate.

Each input object should have enough simulation data in it that the fit_data method outputs the correct fit. The fit_options may be modified for this purpose as well. Before calling this function, make sure that each fit extrapolates well to very low logical error rates.

Parameters:

• memory (Memory) – Perform a memory experiment where the distance is varied.

• magic_state_prep (MagicStatePreparationHookInjection | MagicStatePreparationRepCode) – A magic state prep object where the largest distance of the protocol is varied.

• lattice_surgery_distance (LatticeSurgery) – A lattice surgery object where the distance is varied.

• lattice_surgery_rounds (LatticeSurgery) – A lattice surgery object where the merge round are varied.

Raises:

• ValueError – If multiple noise models are founds in any of the inputs.

• ValueError – If the two lattice surgery have different noise models.

Returns:

A json string with the noise profile.

Return type:

str