Bayesian Optimization for Repeater Protocols

2025-02-04

AI Summary

Key distribution in long-distance heterogeneous quantum repeater networks suffers from low efficiency, a high-dimensional unstructured protocol space, and difficulties in accurately modeling and optimizing the secret key rate. Method: This paper introduces the first scalable numerical framework for computing secret key rates—supporting arbitrary numbers of nodes and hardware heterogeneity—and extends Li et al.’s method to accommodate non-ideal devices. It also pioneers the integration of Bayesian optimization into repeater protocol design to enable efficient global search over complex protocol spaces. Contributions/Results: Experiments demonstrate stable convergence to optimal protocols across multi-node configurations, achieving accuracy comparable to brute-force search. Furthermore, the approach quantitatively uncovers the synergistic impact of node count, entanglement fidelity, and operation success probability on the secret key rate. This work establishes a new paradigm for practical quantum network protocol design—scalable, high-precision, and automated.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Efficiently distributing secret keys over long distances remains a critical challenge in the development of quantum networks."First-generation"quantum repeater chains distribute entanglement by executing protocols composed of probabilistic entanglement generation, swapping and distillation operations. However, finding the protocol that maximizes the secret-key rate is difficult for two reasons. First, calculating the secretkey rate for a given protocol is non-trivial due to experimental imperfections and the probabilistic nature of the operations. Second, the protocol space rapidly grows with the number of nodes, and lacks any clear structure for efficient exploration. To address the first challenge, we build upon the efficient machinery developed by Li et al. [1] and we extend it, enabling numerical calculation of the secret-key rate for heterogeneous repeater chains with an arbitrary number of nodes. For navigating the large, unstructured space of repeater protocols, we implement a Bayesian optimization algorithm, which we find consistently returns the optimal result. Whenever comparisons are feasible, we validate its accuracy against results obtained through brute-force methods. Further, we use our framework to extract insight on how to maximize the efficiency of repeater protocols across varying node configurations and hardware conditions. Our results highlight the effectiveness of Bayesian optimization in exploring the potential of near-term quantum repeater chains.

Problem

• Optimizing secret-key distribution
• Handling probabilistic quantum operations
• Navigating unstructured protocol space

Innovation

• Bayesian optimization algorithm
• numerical secret-key rate calculation
• heterogeneous repeater chains