Alexandru Ioan Cuza University

The work presents a modular benchmarking framework that independently pairs search-direction estimators for parameterized quantum circuits (parameter-shift, finite differences, SPSA, PGPE, observable-curvature preconditioning) with classical update rules (SGD, Adam, RMSprop), evaluating them on MaxCut QAOA, Iris classification, a binary-MNIST QCNN, and molecular-hydrogen VQE under 256-shot finite-sampling simulation plus selected case studies on a 156-qubit processor; results show the preferred update rule depends on the estimator and workload, and that peak and across-seed average performance often favor different configurations.

The work presents a modular benchmarking framework that independently pairs search-direction estimators for parameterized quantum circuits (parameter-shift, finite differences, SPSA, PGPE, observable-curvature preconditioning) with classical update rules (SGD, Adam, RMSprop), evaluating them on MaxCut QAOA, Iris classification, a binary-MNIST QCNN, and molecular-hydrogen VQE under 256-shot finite-sampling simulation plus selected case studies on a 156-qubit processor; results show the preferred update rule depends on the estimator and workload, and that peak and across-seed average performance often favor different configurations.