tensorcircuit.results.counts

dict related functionalities

tensorcircuit.results.counts.count2vec(count: Dict[str, int], normalization: bool = True) → Any

tensorcircuit.results.counts.expectation(count: Dict[str, int], z: Optional[Sequence[int]] = None, diagonal_op: Optional[Any] = None) → float

compute diagonal operator expectation value from bit string count dictionary

Parameters

• count (ct) – count dict for bitstring histogram

• z (Optional[Sequence[int]]) – if defaults as None, then diagonal_op must be set a list of qubit that we measure Z op on

• diagoal_op (Tensor) – shape [n, 2], explicitly indicate the diagonal op on each qubit eg. [1, -1] for z [1, 1] for I, etc.

Returns

the expectation value

Return type

float

tensorcircuit.results.counts.kl_divergence(c1: Dict[str, int], c2: Dict[str, int]) → float

tensorcircuit.results.counts.marginal_count(count: Dict[str, int], keep_list: Sequence[int]) → Dict[str, int]

tensorcircuit.results.counts.normalized_count(count: Dict[str, int]) → Dict[str, float]

tensorcircuit.results.counts.plot_histogram(data: Any, **kws: Any) → Any

See qiskit.visualization.plot_histogram: https://qiskit.org/documentation/stubs/qiskit.visualization.plot_histogram.html

interesting kw options include: number_to_keep (int)

Parameters

data (Any) – _description_

Returns

_description_

Return type

Any

tensorcircuit.results.counts.reverse_count(count: Dict[str, int]) → Dict[str, int]

tensorcircuit.results.counts.sort_count(count: Dict[str, int]) → Dict[str, int]

tensorcircuit.results.counts.vec2count(vec: Any, prune: bool = False) → Dict[str, int]