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QuasiDistribution

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

Bases: dict(opens in a new tab)

A dict-like class for representing quasi-probabilities.

Builds a quasiprobability distribution object.

Note

The quasiprobability values might include floating-point errors. QuasiDistribution.__repr__ rounds using numpy.round() and the parameter ndigits can be manipulated with the class attribute __ndigits__. The default is 15.

Parameters

  • data (dict(opens in a new tab)) –

    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(opens in a new tab)) – Number of shots the distribution was derived from.

  • stddev_upper_bound (float(opens in a new tab)) – An upper bound for the standard deviation

Raises


Attributes

stddev_upper_bound

Return an upper bound on standard deviation of expval estimator.


Methods

binary_probabilities

binary_probabilities(num_bits=None)

Build a quasi-probabilities dictionary with binary string keys

Parameters

num_bits (int(opens in a new tab)) – 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.

Returns

A dictionary where the keys are binary strings in the format

"0110"

Return type

dict(opens in a new tab)

clear

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

copy

copy() → a shallow copy of D

fromkeys

fromkeys(value=None, /)

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

get

get(key, default=None, /)

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

hex_probabilities

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(opens in a new tab)

items

items() → a set-like object providing a view on D's items

keys

keys() → a set-like object providing a view on D's keys

nearest_probability_distribution

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(opens in a new tab)) – 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

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

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

popitem

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

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

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

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

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