Surrogate-guided optimization in quantum networks

When physical architectures become too complex for analytical study, numerical simulation proves essential to investigate quantum network behavior. Although highly informative, these simulations involve intricate numerical functions without known analytical forms, making traditional optimization techniques that assume continuity, differentiability, or convexity inapplicable. We introduce a more efficient computational framework that employs machine learning models as surrogates for the objective function. We demonstrate the effectiveness of our approach by applying it to three well-known optimization problems in quantum networking: allocating quantum memory across multiple nodes, tuning an experimental parameter in every physical link of a quantum entanglement switch, and finding effective protocol configurations in a large asymmetric quantum network. Our algorithm consistently outperforms Simulated Annealing and Bayesian optimization within the allotted time, improving results by up to 29% and 28%, respectively. Our framework will thus allow for more comprehensive quantum network studies, integrating surrogate-assisted optimization with existing quantum network simulators.