Surrogate-guided optimization in quantum networks

TL;DR

This paper introduces a machine learning surrogate-based optimization method to enhance quantum network design, outperforming traditional approaches in efficiency and solution quality for complex, simulation-based problems.

Contribution

It presents a novel surrogate-guided optimization workflow tailored for quantum networks, enabling efficient optimization when analytical models are unavailable and simulations are computationally expensive.

Findings

Surrogate models improve optimization efficiency in quantum networks.

The method outperforms baseline approaches like Simulated Annealing and Bayesian optimization.

Solutions are up to 20% better within the same time constraints.

Abstract

We propose an optimization algorithm to improve the design and performance of quantum communication networks. When physical architectures become too complex for analytical methods, numerical simulation becomes essential to study quantum network behavior. Although highly informative, these simulations involve complex numerical functions without known analytical forms, making traditional optimization techniques that assume continuity, differentiability, or convexity inapplicable. Additionally, quantum network simulations are computationally demanding, rendering global approaches like Simulated Annealing or genetic algorithms, which require extensive function evaluations, impractical. We introduce a more efficient optimization workflow using machine learning models, which serve as surrogates for a given objective function. We demonstrate the effectiveness of our approach by applying it to three well-known optimization problems in quantum networking: quantum memory allocation for multiple network nodes, tuning an experimental parameter in all physical links of a quantum entanglement switch, and finding efficient protocol settings within a large asymmetric quantum network. The solutions found by our algorithm consistently outperform those obtained with our baseline approaches -- Simulated Annealing and Bayesian optimization -- in the allotted time limit by up to 18\% and 20\%, respectively. Our framework thus allows for more comprehensive quantum network studies, integrating surrogate-assisted optimization with existing quantum network simulators.