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.
Sample-Efficient Tuning of Quantum Circuit Parameters via Bayesian Optimization / Pannone, Alessandro; Tosone, Federico; Faccini, Daniel; Romito, Francesco; Mazzi, Nicolò. - (2025). (Intervento presentato al convegno ODS2025 – International Conference on Optimization and Decision Science tenutosi a Milan; Italy).
Sample-Efficient Tuning of Quantum Circuit Parameters via Bayesian Optimization
Alessandro Pannone;Francesco Romito;
2025
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
