LogicalQ.Estimators

Functions

compute_constraint_model(hardware_model, label, ...[, ...])	Compute constraint model for a given quantum error correction code.
compute_effective_threshold(hardware_model, label, ...)	Compute effective threshold for a given quantum error correction code.
compute_gadget_costs([gadgets_library, backend, ...])	Compute the costs of a gadgets library based on a hardware model or a backend.
build_gadgets_library(min_depth, max_depth, ...)	Build a library of gadgets within the specified depth and qubit count parameters.

Module Contents

LogicalQ.Estimators.compute_constraint_model(hardware_model, label, stabilizer_tableau, optimizer=True, effective_threshold=None, gadget_costs=None, constraint_model=None)

Compute constraint model for a given quantum error correction code.

Parameters:

• hardware_model (dict)

• label (Iterable[int])

• stabilizer_tableau (Iterable[str])

• optimizer (bool)

• effective_threshold (LogicalQ.Typing.Number | None)

• gadget_costs (dict | None)

• constraint_model (dict | None)

Return type:

dict

LogicalQ.Estimators.compute_effective_threshold(hardware_model, label, stabilizer_tableau, initial_states=None, min_theta=0, max_theta=np.pi / 2, n_theta=16, min_phi=0, max_phi=np.pi, n_phi=32, max_n_qec_cycles=1, shots=10000.0, threshold_conditions=None)

Compute effective threshold for a given quantum error correction code.

Parameters:

• hardware_model (dict)

• label (Iterable[int])

• stabilizer_tableau (Iterable[str])

• initial_states (Iterable[str] | None)

• min_theta (LogicalQ.Typing.Number)

• max_theta (LogicalQ.Typing.Number)

• n_theta (int)

• min_phi (LogicalQ.Typing.Number)

• max_phi (LogicalQ.Typing.Number)

• n_phi (int)

• max_n_qec_cycles (int)

• shots (LogicalQ.Typing.Number)

• threshold_conditions (Iterable[Callable[[float, float], bool]] | None)

Return type:

tuple[dict, dict, float]

LogicalQ.Estimators.compute_gadget_costs(gadgets_library=None, backend=None, hardware_model=None)

Compute the costs of a gadgets library based on a hardware model or a backend.

Parameters:

• gadgets_library (dict | None)

• backend (LogicalQ.Typing.QuantumBackend | None)

• hardware_model (dict | None)

Return type:

tuple[dict, dict, dict]

LogicalQ.Estimators.build_gadgets_library(min_depth, max_depth, step_depth, min_n_qubits, max_n_qubits, step_n_qubits)

Build a library of gadgets within the specified depth and qubit count parameters.

Parameters:

• min_depth (int)

• max_depth (int)

• step_depth (int)

• min_n_qubits (int)

• max_n_qubits (int)

• step_n_qubits (int)

Return type:

dict