Sample-Efficient Tuning of Quantum Circuit Parameters via Bayesian Optimization
Alessandro Pannone, Federico Tosone, Daniel Faccini, Francesco Romito, Nicolò Mazzi · IRIS Research product catalog (Sapienza University of Rome) · 2025
In this work, we present a Bayesian Optimization (BO) approach for tuning the parameters of the Quantum Approximate Optimization Algorithm (QAOA) applied to max-cut problems. Within our black-box optimization framework, BO achieves competitive solutions while requiring significantly fewer quantum circuit evaluations compared to standard non-Bayesian global optimizers. These results highlight the potential of BO to enhance both the efficiency and the overall performance of variational quantum algorithms.