quara.qcircuit.data_generator module

calc_empi_dist_sequence(measurement_num, data, num_sums)[source]

calculates empirical distributions.

uses data from 0-th to num_sums[index]-th to calculate empirical distributions.

Parameters

  • measurement_num (int) – number of measurements.

  • data (List[int]) – data of measurement outcomes.

  • num_sums (List[int]) – a list of the range of data to calculate empirical distributions.

Returns

a list of (the range of data, empirical distribution). the dtype of each empirical distribution is np.float64.

Return type

List[Tuple[int, np.ndarray]]

Raises

  • ValueError – measurement_num is not non-negative integer.

  • ValueError – there is an element of num_sums that is not less than or equal to length of data.

  • ValueError – there is an element of data that is not non-negative and less than measurement_num.

  • ValueError – num_sums is not an increasing sequence.

Examples

>>> measurement_num = 2
>>> data = [1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1]
>>> num_sums = [5, 10, 20]
>>> empi_dist = calc_empi_dist_sequence(measurement_num, data, num_sums)
>>> empi_dist
[(5, array([0.4, 0.6])), (10, array([0.3, 0.7])), (20, array([0.3, 0.7]))]
calc_empi_dists_sequence(measurement_nums, dataset, list_num_sums)[source]

calculates a sequence of empirical distributions by calc_empidist().

Parameters

  • measurement_nums (List[int]) – a list of measurement_num

  • dataset (List[List[int]]) – a dataset

  • list_num_sums (List[List[int]]) – a list of num_sums

Returns

a sequence of empirical distributions.

Return type

List[List[np.ndarray]]

Raises

  • ValueError – the length of measurement_nums does not equal the length of dataset.

  • ValueError – the length of measurement_nums does not equal the length of list_llist_num_sumsist_num_sum.

generate_data_from_prob_dist(prob_dist, data_num, seed_or_generator=None, atol=None)[source]

generates random data from a probability distribution.

the data is a sequence (list) of measurement outcomes. measurement outcomes are integers. 0 <= each measurement outcomes < len(probdist). length of the data equals data_num.

Parameters

  • prob_dist (np.ndarray) – a probability distribution used to generate random data.

  • data_num (int) – length of the data.

  • seed_or_generator (Union[int, np.random.Generator], optional) – If the type is int, it is assumed to be a seed used to generate random data. If the type is Generator, it is used to generate random data. If argument is None, np.random is used to generate random data. Default value is None.

  • atol (float, optional) – the absolute tolerance parameter, uses get_atol() by default. checks absolute(the sum of probabilities - 1) <= atol in this function.

Returns

generated data.

Return type

List[int]

Raises

  • ValueError – each probability is not a positive number.

  • ValueError – the sum of probabilities does not equal 1.

generate_dataset_from_prob_dists(prob_dists, data_nums, seeds_or_generators=None)[source]

generates random dataset from probability distributions.

the dataset is a list of data generated by generate_data_from_probdist()

Parameters

  • prob_dists (List[np.ndarray]) – a list of probdist.

  • data_nums (List[int]) – a list of data_num.

  • seeds_or_generators (Union[int, np.random.Generator], optional) – If the type is int, generates Generator with seed seed_or_generators and returned generated Generator. If the type is Generator, returns Generator. If argument is None, returns np.random. Default value is None.

Returns

generated dataset.

Return type

List[List[int]]

Raises

  • ValueError – the length of prob_dists does not equal the length of data_nums.

  • ValueError – seeds_or_streams is not None and the length of prob_dists does not equal the length of seeds_or_streams.

generate_empi_dist_sequence_from_prob_dist(prob_dist, num_sums, seed_or_generator=None)[source]

calculates a sequence of empirical distribution from probability distribution.

Parameters

  • prob_dist (np.ndarray) – probability distribution.

  • num_sums (List[int]) – list of number of trials

  • seed_or_generator (Union[int, np.random.Generator], optional) – If the type is int, it is assumed to be a seed used to generate random data. If the type is Generator, it is used to generate random data. If argument is None, np.random is used to generate random data. Default value is None.

Returns

a sequence of empirical distribution.

Return type

List[Tuple[int, np.ndarray]]

generate_empi_dists_sequence_from_prob_dists(prob_dists, list_num_sums, seed_or_generator=None)[source]

calculates a sequence of empirical distributions from probability distributions.

Parameters

  • prob_dists (List[np.ndarray]) – probability distributions.

  • list_num_sums (List[List[int]]) – list of number of trials

  • seed_or_generator (Union[int, np.random.Generator], optional) – If the type is int, it is assumed to be a seed used to generate random data. If the type is Generator, it is used to generate random data. If argument is None, np.random is used to generate random data. Default value is None.

Returns

a sequence of empirical distributions.

Return type

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

Raises

ValueError – the length of prob_dists does not equal the length of list_num_sums.