topqad_sdk.noiseprofiler package

Subpackages

• topqad_sdk.noiseprofiler.libnoise package
  • PhysicalDepolarizing
    • PhysicalDepolarizing.calculate_stabilization_time()
    • PhysicalDepolarizing.from_dict()
    • PhysicalDepolarizing.from_preset()
    • PhysicalDepolarizing.noise_model_name
    • PhysicalDepolarizing.strength_gate_operation_noise_channel()
    • PhysicalDepolarizing.strength_idle_qubit_noise_channel()
    • PhysicalDepolarizing.strength_idle_qubit_stochastic_noise_channel()
    • PhysicalDepolarizing.strength_spam_operation_noise_channel()
  • UniformDepolarizing
    • UniformDepolarizing.calculate_stabilization_time()
    • UniformDepolarizing.from_dict()
    • UniformDepolarizing.noise_model_name
• topqad_sdk.noiseprofiler.libprotocols package
  • LatticeSurgery
    • LatticeSurgery.add_instance()
    • LatticeSurgery.add_noise_model()
    • LatticeSurgery.cancel()
    • LatticeSurgery.execute_simulation()
    • LatticeSurgery.fit_data()
    • LatticeSurgery.fit_options
    • LatticeSurgery.includes_postselection
    • LatticeSurgery.load()
    • LatticeSurgery.noise_models
    • LatticeSurgery.plot()
    • LatticeSurgery.protocol_category
    • LatticeSurgery.protocol_name
    • LatticeSurgery.protocol_parameters
    • LatticeSurgery.protocol_subcategory
    • LatticeSurgery.set_simulation_parameters()
    • LatticeSurgery.simulation_table
    • LatticeSurgery.update_results()
    • LatticeSurgery.valid_observables
  • MagicStatePreparationHookInjection
    • MagicStatePreparationHookInjection.add_instance()
    • MagicStatePreparationHookInjection.add_noise_model()
    • MagicStatePreparationHookInjection.cancel()
    • MagicStatePreparationHookInjection.execute_simulation()
    • MagicStatePreparationHookInjection.fit_data()
    • MagicStatePreparationHookInjection.fit_options
    • MagicStatePreparationHookInjection.includes_postselection
    • MagicStatePreparationHookInjection.load()
    • MagicStatePreparationHookInjection.noise_models
    • MagicStatePreparationHookInjection.plot()
    • MagicStatePreparationHookInjection.protocol_category
    • MagicStatePreparationHookInjection.protocol_name
    • MagicStatePreparationHookInjection.protocol_parameters
    • MagicStatePreparationHookInjection.protocol_subcategory
    • MagicStatePreparationHookInjection.set_simulation_parameters()
    • MagicStatePreparationHookInjection.simulation_table
    • MagicStatePreparationHookInjection.update_results()
  • MagicStatePreparationRepCode
    • MagicStatePreparationRepCode.add_instance()
    • MagicStatePreparationRepCode.add_noise_model()
    • MagicStatePreparationRepCode.cancel()
    • MagicStatePreparationRepCode.execute_simulation()
    • MagicStatePreparationRepCode.fit_data()
    • MagicStatePreparationRepCode.fit_options
    • MagicStatePreparationRepCode.includes_postselection
    • MagicStatePreparationRepCode.load()
    • MagicStatePreparationRepCode.noise_models
    • MagicStatePreparationRepCode.plot()
    • MagicStatePreparationRepCode.protocol_category
    • MagicStatePreparationRepCode.protocol_name
    • MagicStatePreparationRepCode.protocol_parameters
    • MagicStatePreparationRepCode.protocol_subcategory
    • MagicStatePreparationRepCode.set_simulation_parameters()
    • MagicStatePreparationRepCode.simulation_table
    • MagicStatePreparationRepCode.update_results()
  • Memory
    • Memory.add_instance()
    • Memory.add_noise_model()
    • Memory.cancel()
    • Memory.execute_simulation()
    • Memory.fit_data()
    • Memory.fit_options
    • Memory.includes_postselection
    • Memory.load()
    • Memory.noise_models
    • Memory.plot()
    • Memory.protocol_category
    • Memory.protocol_name
    • Memory.protocol_parameters
    • Memory.protocol_subcategory
    • Memory.set_simulation_parameters()
    • Memory.simulation_table
    • Memory.update_results()
  • Stability
    • Stability.add_instance()
    • Stability.add_noise_model()
    • Stability.cancel()
    • Stability.execute_simulation()
    • Stability.fit_data()
    • Stability.fit_options
    • Stability.includes_postselection
    • Stability.load()
    • Stability.noise_models
    • Stability.plot()
    • Stability.protocol_category
    • Stability.protocol_name
    • Stability.protocol_parameters
    • Stability.protocol_subcategory
    • Stability.set_simulation_parameters()
    • Stability.simulation_table
    • Stability.update_results()
  • topqad_sdk.noiseprofiler.libprotocols models
    • ModelLatticeSurgery
      • ModelLatticeSurgery.distance
      • ModelLatticeSurgery.bus_width
      • ModelLatticeSurgery.rounds
      • ModelLatticeSurgery.merge_observable
      • ModelLatticeSurgery.preparation_basis
      • ModelLatticeSurgery.measurement_basis
      • ModelLatticeSurgery.logical_observable
    • ModelMagicStatePreparationHookInjection
      • ModelMagicStatePreparationHookInjection.d_1
      • ModelMagicStatePreparationHookInjection.d_2
      • ModelMagicStatePreparationHookInjection.r_2
      • ModelMagicStatePreparationHookInjection.inject_state
    • ModelMagicStatePreparationRepCode
      • ModelMagicStatePreparationRepCode.distances
      • ModelMagicStatePreparationRepCode.rounds
      • ModelMagicStatePreparationRepCode.inject_state
    • ModelMemory
      • ModelMemory.distance
      • ModelMemory.rounds
      • ModelMemory.basis
    • ModelStability
      • ModelStability.rounds
      • ModelStability.diameter
• topqad_sdk.noiseprofiler.qre_noiseprofile package
  • get_preset_names()
  • noise_profile_from_preset()
  • noise_profile_from_protocols()

Module contents

class topqad_sdk.noiseprofiler.FitSpecification(fit_ansatz: str, param_bounds: tuple[list, list], y_scale: str, fit_ansatz_latex: str, ind_math_symbol: str)

Contains all information to specify a fit for a given class of curves.

fit_ansatz

The fit function.

Type:

Callable

param_bounds

The parameter bounds.

Type:

tuple[list, list]

y_scale

Either ‘linear’ or ‘log’.

Type:

str

fit_ansatz_latex

The latex description of the fit function. Use raw strings.

Type:

str

Examples

def fit_ansatz_memory_d_ler(distance, p_1, p_2):

return -(distance+1)/2 * np.log(p_2) + 2*np.log(distance) + np.log(p_1)

FitSpecification(fit_ansatz=fit_ansatz_memory_d_ler,

param_bounds=([0, 0], [np.inf, np.inf]), y_scale=’log’, fit_ansatz_latex=r”{p_1} d^2 times {p_2}^{{-frac{{d+1}}{{2}}}}”, )

fit_ansatz: str

fit_ansatz_latex: str

ind_math_symbol: str

param_bounds: tuple[list, list]

y_scale: str

class topqad_sdk.noiseprofiler.Quantity(*, value: float, unit: str)

A basic quantity with a value and unit.

value

The numerical value of the quantity.

Type:

float

unit

The unit of the quantity (e.g., ‘ns’, ‘μs’, ‘ms’).

Type:

str

classmethod from_raw_value(raw_value: float, unit: str) → Quantity

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

unit: str

value: float