cirq.experiments.least_squares_xeb_fidelity_from_probabilities

Least squares fidelity estimator with observable based on probabilities.

cirq.experiments.least_squares_xeb_fidelity_from_probabilities(
    hilbert_space_dimension: int,
    observed_probabilities: Sequence[Sequence[float]],
    all_probabilities: Sequence[Sequence[float]],
    observable_from_probability: Optional[Callable[[float], float]] = None,
    normalize_probabilities: bool = True
) -> Tuple[float, List[float]]

Using the notation from the docstring of least_squares_xeb_fidelity_from_expectations, this function computes the least squares fidelity estimate when the observable O_U has eigenvalue corresponding to the computational basis state |z⟩ given by g(p(z)), where p(z) = |⟨z|𝜓_U⟩|^2 and g is a function that can be specified. By default, g is the identity function, but other choices, such as the logarithm, are useful. By default, the probability p(z) is actually multiplied by the Hilbert space dimension D, so that the observable is actually g(D * p(z)). This behavior can be disabled by setting normalize_probabilities to False.

hilbert_space_dimension Dimension of the Hilbert space on which the channel whose fidelity is being estimated is defined.
observed_probabilities Ideal probabilities of bitstrings observed in experiments. A list of lists, where each inner list contains the probabilities for a single circuit.
all_probabilities Ideal probabilities of all possible bitstrings. A list of lists, where each inner list contains the probabilities for a single circuit, and should have length equal to the Hilbert space dimension. The order of the lists should correspond to that of observed_probabilities.
observable_from_probability Function that computes the observable from a given probability.
normalize_probabilities Whether to multiply the probabilities by the Hilbert space dimension before computing the observable.

A tuple of two values. The first value is the estimated fidelity. The second value is a list of the residuals

f (e_U - u_U) - (m_U - u_U)

of the least squares minimization.