qiskit.result.QuasiDistribution

class QuasiDistribution(data, shots=None)

A dict-like class for representing qasi-probabilities.

Warning

This is an unsupported class in the current 0.17.x release series. It is present for compatibility with the qiskit-ibmq-provider’s beta qiskit runtime support, but this interface isn’t guaranteed to be stable when moving to >=0.18.0 and likely will change.

Builds a quasiprobability distribution object.

Parameters

• data (dict) – Input quasiprobability data.

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

__init__(data, shots=None)

Builds a quasiprobability distribution object.

Parameters

• data (dict) – Input quasiprobability data.

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

Methods

__init__(data[, shots])	Builds a quasiprobability distribution object.
clear()
copy()
fromkeys([value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
nearest_probability_distribution([…])	Takes a quasiprobability distribution and maps it to the closest probability distribution as defined by the L2-norm.
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
popitem()	2-tuple; but raise KeyError if D is empty.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
update([E, ]**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()

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.

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)

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(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() → (k, v), remove and return some (key, value) pair as a

2-tuple; but raise KeyError if D 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