qiskit.result.ProbDistribution

class ProbDistribution(data, shots=None)

A generic dict-like class for probability distributions.

Builds a probability distribution object.

Parameters

• data (dict) –

  Input probability data. Where the keys represent a measured classical value and the value is a float for the probability 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.

Raises

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

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

__init__(data, shots=None)

Builds a probability distribution object.

Parameters

• data (dict) –

  Input probability data. Where the keys represent a measured classical value and the value is a float for the probability 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.

Raises

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

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

Methods

__init__(data[, shots])	Builds a probability distribution object.
binary_probabilities([num_bits])	Build a probabilities dictionary with binary string keys
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.
hex_probabilities()	Build a probabilities dictionary with hexadecimal string keys
items()
keys()
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()

binary_probabilities(num_bits=None)

Build a 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() → 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()

Build a probabilities dictionary with hexadecimal string keys

Returns

A dictionary where the keys are hexadecimal strings in the

format "0x1a"

Return type

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

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