StochasticFidelityMetricEstimator

class StochasticFidelityMetricEstimator

Bases: MetricEstimator

Stochastic Fubini–Study metric via state-overlap fidelities (QN-SPSA).

Provides a "fidelity_fn" evaluator rather than a closed-form "metric_fn": the QN-SPSA optimizer reconstructs the metric from finite differences of the state fidelity \(F(\theta_1,\theta_2)=|\langle\psi(\theta_1)|\psi(\theta_2)\rangle|^2\), estimated as the all-zeros probability of the compute-uncompute circuit \(U(\theta_1)\,U(\theta_2)^\dagger\). Like the Fubini–Study metric it is the geometry of the ansatz state — independent of the loss observable — so it applies to any qiskit-invertible ansatz. The overlap circuits are built by a preprocessor from the program’s normal post-spec ansatz cohort and averaged over preserved pipeline axes.

Methods Summary

bind(program)	Return the evaluators this metric provides, keyed by name.
check_compatible(program)	Raise ContractViolation if this metric cannot be applied to program.

Methods Documentation

bind(program)

Return the evaluators this metric provides, keyed by name.

Deterministic estimators (pullback, Fubini–Study) provide "metric_fn" — a pure function of the parameters returning the metric matrix — and the pullback estimator additionally returns the loss gradient under "jac". The stochastic-fidelity estimator instead provides "fidelity_fn": the QN-SPSA optimizer builds its metric from finite differences of that fidelity rather than from a closed-form matrix. The variational algorithm forwards whichever keys appear to the optimizer; keys absent fall back to the algorithm’s parameter-shift defaults.

Return type:

dict[str, Callable[..., Any]]

check_compatible(program)

Raise ContractViolation if this metric cannot be applied to program. Called at run() start so an incompatible pairing fails loudly before any optimization.

Return type:

None