qiskit.result.QuasiDistribution
class QuasiDistribution(data, shots=None)
A dict-like class for representing qasi-probabilities.
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__
__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
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.
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) – Return the L2 distance between distributions.
Returns
Nearest probability distribution. float: Euclidean (L2) distance of distributions.
Return type
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, d is returned if given, otherwise KeyError is raised
popitem
popitem() → (k, v), remove and return some (key, value) pair as a
2-tuple; but raise KeyError if D 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