Counts (qiskit_addon_sqd.counts)

Functions for transforming counts dictionaries.

counts_to_arrays(counts)

Convert a counts dictionary into a bitstring matrix and a probability array.

Parameters:

counts (dict[str, float | int]) – The counts dictionary to convert

Returns:

• A 2D array representing the sampled bitstrings. Each row represents a bitstring, and each element is a bool representation of the bit’s value

• A 1D array containing the probability with which each bitstring was sampled

Return type:

tuple[ndarray, ndarray]

generate_counts_uniform(num_samples, num_bits, rand_seed=None)

Generate a bitstring counts dictionary of samples drawn from the uniform distribution.

Parameters:

• num_samples (int) – The number of samples to draw

• num_bits (int) – The number of bits in the bitstrings

• rand_seed (Generator | int | None) – A seed for controlling randomness

Returns:

A dictionary mapping bitstrings of length num_bits to the number of times they were sampled.

Raises:

ValueError – num_samples and num_bits must be positive integers.

Return type:

dict[str, int]

generate_counts_bipartite_hamming(num_samples, num_bits, *, hamming_right, hamming_left, rand_seed=None)

Generate a bitstring counts dictionary with specified bipartite hamming weight.

Parameters:

• num_samples (int) – The number of samples to draw

• num_bits (int) – The number of bits in the bitstrings

• hamming_right (int) – The hamming weight on the right half of each bitstring

• hamming_left (int) – The hamming weight on the left half of each bitstring

• rand_seed (Generator | int | None) – A seed for controlling randomness

Returns:

A dictionary mapping bitstrings to the number of times they were sampled. Each half of each bitstring in the output dictionary will have a hamming weight as specified by the inputs.

Raises:

• ValueError – num_bits and num_samples must be positive integers.

• ValueError – Hamming weights must be specified as non-negative integers.

• ValueError – num_bits must be even.

Return type:

dict[str, int]

normalize_counts_dict(counts)

Convert a counts dictionary into a probability dictionary.

Parameters:

counts (dict[str, float | int])

Return type:

dict[str, float]