QuasiDistribution#

class qiskit.result.QuasiDistribution(data, shots=None, stddev_upper_bound=None)[ソース]#

ベースクラス: dict

A dict-like class for representing quasi-probabilities.

Builds a quasiprobability distribution object.

パラメータ:

  • data (dict) –

    Input quasiprobability data. Where the keys represent a measured classical value and the value is a float for the quasiprobability of that result. The keys can be one of several formats:

    • A hexadecimal string of the form "0x4a"

    • A bit string e.g. '0b1011' or "01011"

    • An integer

  • shots (int) – Number of shots the distribution was derived from.

  • stddev_upper_bound (float) – An upper bound for the standard deviation

例外:

  • TypeError – If the input keys are not a string or int

  • ValueError – If the string format of the keys is incorrect

Attributes

stddev_upper_bound#

Return an upper bound on standard deviation of expval estimator.

Methods

binary_probabilities(num_bits=None)[ソース]#

Build a quasi-probabilities dictionary with binary string keys

パラメータ:

num_bits (int) – number of bits in the binary bitstrings (leading zeros will be padded). If None, a default value will be used. If keys are given as integers or strings with binary or hex prefix, the default value will be derived from the largest key present. If keys are given as bitstrings without prefix, the default value will be derived from the largest key length.

戻り値:

A dictionary where the keys are binary strings in the format

"0110"

戻り値の型:

dict

clear() → None.  Remove all items from D.#
copy() → a shallow copy of D#
fromkeys(value=None, /)#

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#

Return the value for key if key is in the dictionary, else default.

hex_probabilities()[ソース]#

Build a quasi-probabilities dictionary with hexadecimal string keys

戻り値:

A dictionary where the keys are hexadecimal strings in the

format "0x1a"

戻り値の型:

dict

items() → a set-like object providing a view on D's items#
keys() → a set-like object providing a view on D's keys#
nearest_probability_distribution(return_distance=False)[ソース]#

Takes a quasiprobability distribution and maps it to the closest probability distribution as defined by the L2-norm.

パラメータ:

return_distance (bool) – Return the L2 distance between distributions.

戻り値:

Nearest probability distribution. float: Euclidean (L2) distance of distributions.

戻り値の型:

ProbDistribution

メモ

Method from Smolin et al., Phys. Rev. Lett. 108, 070502 (2012).

pop(k[, d]) → v, remove specified key and return the corresponding value.#

If key is not found, d is returned if given, otherwise KeyError is raised

popitem()#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.#

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values#