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QuasiDistribution

QuasiDistribution(data, shots=None, stddev_upper_bound=None) GitHub(opens in a new tab)

Bases: dict

A dict-like class for representing qasi-probabilities.

Builds a quasiprobability distribution object.

Parameters

  • 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

Raises

  • TypeError – If the input keys are not a string or int
  • ValueError – If the string format of the keys is incorrect

Methods

binary_probabilities

QuasiDistribution.binary_probabilities(num_bits=None)

Build a quasi-probabilities dictionary with binary string keys

Parameters

num_bits (int) – number of bits in the binary bitstrings (leading zeros will be padded). If None, the length will be derived from the largest key present.

Returns

A dictionary where the keys are binary strings in the format

"0110"

Return type

dict

clear

QuasiDistribution.clear() → None. Remove all items from D.

copy

QuasiDistribution.copy() → a shallow copy of D

fromkeys

QuasiDistribution.fromkeys(value=None, /)

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

get

QuasiDistribution.get(key, default=None, /)

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

hex_probabilities

QuasiDistribution.hex_probabilities()

Build a quasi-probabilities dictionary with hexadecimal string keys

Returns

A dictionary where the keys are hexadecimal strings in the

format "0x1a"

Return type

dict

items

QuasiDistribution.items() → a set-like object providing a view on D’s items

keys

QuasiDistribution.keys() → a set-like object providing a view on D’s keys

nearest_probability_distribution

QuasiDistribution.nearest_probability_distribution(return_distance=False)

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

Parameters

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

Returns

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

Return type

ProbDistribution

Notes

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

pop

QuasiDistribution.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

QuasiDistribution.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

QuasiDistribution.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

QuasiDistribution.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

QuasiDistribution.values() → an object providing a view on D’s values


Attributes

stddev_upper_bound

Return an upper bound on standard deviation of expval estimator.

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