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Least squares fidelity estimator.
cirq.least_squares_xeb_fidelity_from_expectations( measured_expectations: Sequence[float], exact_expectations: Sequence[float], uniform_expectations: Sequence[float] ) -> Tuple[float, List[float]]
An XEB experiment collects data from the execution of random circuits subject to noise. The effect of applying a random circuit with unitary U is modeled as U followed by a depolarizing channel. The result is that the initial state |𝜓⟩ is mapped to a density matrix ρ_U as follows:
|𝜓⟩ → ρ_U = f |𝜓_U⟩⟨𝜓_U| + (1 - f) I / D
where |𝜓_U⟩ = U|𝜓⟩, D is the dimension of the Hilbert space, I / D is the maximally mixed state, and f is the fidelity with which the circuit is applied. Let O_U be an observable that is diagonal in the computational basis. Then the expectation of O_U on ρ_U is given by
Tr(ρ_U O_U) = f ⟨𝜓_U|O_U|𝜓_U⟩ + (1 - f) Tr(O_U / D).
This equation shows how f can be estimated, since Tr(ρ_U O_U) can be estimated from experimental data, and ⟨𝜓_U|O_U|𝜓_U⟩ and Tr(O_U / D) can be computed numerically.
Let e_U = ⟨𝜓_U|O_U|𝜓_U⟩, u_U = Tr(O_U / D), and m_U denote the experimental estimate of Tr(ρ_U O_U). Then we estimate f by performing least squares minimization of the quantity
f (e_U - u_U) - (m_U - u_U)
over different random circuits (giving different U). The solution to the least squares problem is given by
f = (∑_U (m_U - u_U) * (e_U - u_U)) / (∑_U (e_U - u_U)^2).
||A sequence of the m_U, the experimental estimates of the observable, one for each circuit U.|
A sequence of the e_U, the exact value of the
observable. The order should match the order of the
||A sequence of the u_U, the expectation of the observable on a uniformly random bitstring. The order should match the order in the other arguments.|
A tuple of two values. The first value is the estimated fidelity.
The second value is a list of the residuals
of the least squares minimization.
||The lengths of the input sequences are not all the same.|