quara.protocol.qtomography.standard.standard_povmt module

class StandardPovmt(states, num_outcomes, is_physicality_required=False, is_estimation_object=False, on_para_eq_constraint=False, eps_proj_physical=None, seed=None, schedules='all')[source]

Bases: quara.protocol.qtomography.standard.standard_qtomography.StandardQTomography

initialize standard quantum tomography class.

To inherit from this class, set the following instance variables in the constructor of the subclass.

  • _coeffs_0th: return value of get_coeffs_0th function.

  • _coeffs_1st: return value of get_coeffs_1st function.

  • _map_experiment_to_setqoperations: a map from indices of Experiment to indices of SetQOperations.

    if you map the 0th state to the 1st state, set {("state", 0): ("state", 1)}.

  • _map_setqoperations_to_experiment: a map from indices of SetQOperations to indices of Experiment.

Parameters

  • experiment (Experiment) – Experiment class used in quantum tomography.

  • set_qoperations (SetQOperations) – SetQOperations class used in quantum tomography.

  • states (List[quara.objects.state.State]) –

  • num_outcomes (int) –

  • is_physicality_required (bool) –

  • is_estimation_object (bool) –

  • on_para_eq_constraint (bool) –

  • eps_proj_physical (float) –

  • seed (int) –

  • schedules (Union[str, List[List[Tuple]]]) –

calc_cramer_rao_bound(var, N, list_N)[source]

calculates Cramer-Rao bound.

Parameters

  • var (Union[QOperation, np.ndarray]) – variables to calculate Cramer-Rao bound.

  • N (int) – representative value of the number of data.

  • list_N (List[int]) – the number of data for each schedule.

Returns

Cramer-Rao bound.

Return type

np.ndarray

convert_var_to_qoperation(var)[source]

converts variable to QOperation.

this function must be implemented in the subclass.

Parameters

var (np.ndarray) – variables.

Returns

converted QOperation.

Return type

QOperation

Raises

NotImplementedError – this function does not be implemented in the subclass.

estimation_object_type()[source]
Return type

type

generate_empi_dist(schedule_index, povm, num_sum)[source]

Generate empirical distribution using the data generated from probability distribution of specified schedules.

Parameters

  • schedule_index (int) – schedule index.

  • povm (Povm) – true object.

  • num_sum (int) – the number of data to use to generate the experience distributions for each schedule.

Returns

Generated empirical distribution.

Return type

Tuple[int, np.ndarray]

generate_empi_dists(povm, num_sum)[source]

Generate empirical distributions using the data generated from probability distributions of all schedules.

see generate_empi_dists()

Parameters
Return type

List[Tuple[int, numpy.ndarray]]

generate_empi_dists_sequence(povm, num_sums)[source]
Parameters
Return type

List[List[Tuple[int, numpy.ndarray]]]

generate_empty_estimation_obj_with_setting_info()[source]

generates the empty estimation object with setting information.

Returns

the empty estimation object(Povm) with setting information.

Return type

QOperation

is_valid_experiment()[source]

returns whether the experiment is valid.

this function must be implemented in the subclass.

Returns

whether the experiment is valid.

Return type

bool

Raises

NotImplementedError – this function does not be implemented in the subclass.

num_outcomes(schedule_index)[source]

returns the number of outcomes of probability distribution of a schedule index.

Parameters

schedule_index (int) –

Returns

the number of outcomes

Return type

int

property on_para_eq_constraint