quara.protocol.qtomography.qtomography module

class QTomography(experiment, set_qoperations)[source]

Bases: object

initialize quantum tomography class.

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

_num_variables: sum of the number of all variables.

Parameters

experiment (Experiment) – Experiment class used in quantum tomography, which is supposed to have tester objects.
set_qoperations (SetQOperations) – SetQOperations class used in quantum tomography, which is supposed to have conditions of true object.

abstract calc_prob_dist(qope, schedule_index)[source]

calculates a probability distribution.

this function must be implemented in the subclass.

Parameters

qope (QOperation) – QOperation to calculate a probability distribution.
schedule_index (int) – schedule index.

Returns

a probability distribution.

Return type

List[np.ndarray]

Raises

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

abstract calc_prob_dists(qope)[source]

calculates probability distributions.

this function must be implemented in the subclass.

Parameters: qope (QOperation) – QOperation to calculate probability distributions.
Returns: probability distributions.
Return type: List[List[np.ndarray]]
Raises: NotImplementedError – this function does not be implemented in the subclass.

property experiment: quara.qcircuit.experiment.Experiment

returns Experiment class.

Returns: Experiment class.
Return type: Experiment

abstract func_prob_dist()[source]

abstract func_prob_dists()[source]

property gates: List[quara.objects.gate.Gate]

abstract generate_dataset(data_nums)[source]

Run all the schedules to caluclate the probability distribution and generate random data.

this function must be implemented in the subclass.

Parameters: data_nums (List[int]) – A list of the number of data to be generated in each schedule. This parameter should be a list of non-negative integers.
Returns: Generated dataset.
Return type: List[List[np.ndarray]]
Raises: NotImplementedError – this function does not be implemented in the subclass.

abstract generate_empi_dists(qoperation, num_sum)[source]

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

this function must be implemented in the subclass.

Parameters

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

Returns

A list of tuples for the number of data and experience distributions for each schedules.

Return type

List[Tuple[int, np.ndarray]]

Raises

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

abstract 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.

property mprocesses: List[quara.objects.mprocess.MProcess]

property num_schedules: int

returns number of schedules.

Returns: number of schedules.
Return type: int

property num_variables: int

returns sum of the number of all variables.

Returns: sum of the number of all variables.
Return type: int

property povms: List[quara.objects.povm.Povm]

reset_seed(seed=None)[source]

reset new seed.

if seed is None, reset by seed which Experiment already has.

Parameters: seed (int, optional) – new seed, None by default.
Return type: None

property set_qoperations: quara.objects.qoperations.SetQOperations

returns SetQOperations class.

Returns: SetQOperations class.
Return type: SetQOperations

property states: List[quara.objects.state.State]

property testers: List[quara.objects.qoperation.QOperation]

returns tester objects.

Returns: tester objects.
Return type: List[QOperation]
Raises: NotImplementedError – this function does not be implemented in the subclass.