Optimizing Entanglement Generation and Distribution Using Genetic Algorithms

Abstract

Long-distance quantum communication via entanglement distribution is of great importance for the quantum internet. However, scaling up to such long distances has proved challenging due to the loss of photons, which grows exponentially with the distance covered. Quantum repeaters could in theory be used to extend the distances over which entanglement can be distributed, but in practice hardware quality is still lacking. Furthermore, it is generally not clear how an improvement in a certain repeater parameter, such as memory quality or attempt rate, impacts the overall network performance, rendering the path towards scalable quantum repeaters unclear. In this work we propose a methodology based on genetic algorithms and simulations of quantum repeater chains for optimization of entanglement generation and distribution. By applying it to simulations of several different repeater chains, including real-world fiber topology, we demonstrate that it can be used to answer questions such as what are the minimum viable quantum repeaters satisfying given network performance benchmarks. This methodology constitutes an invaluable tool for the development of a blueprint for a pan-European quantum internet. We have made our code, in the form of NetSquid simulations and the smart-stopos optimization tool, freely available for use either locally or on high-performance computing centers.

arXiv commentFixed typos

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• On the design and analysis of near-term quantum network protocols using Markov decision processes
• ReQuSim: Faithfully simulating near-term quantum repeaters
• Optimal resource allocation for flexible-grid entanglement distribution networks
• Reducing classical communication costs in multiplexed quantum repeaters using hardware-aware quasi-local policies
• Hardware requirements for trapped-ion based verifiable blind quantum computing with a measurement-only client
• Optimization of Quantum-Repeater Networks using Stochastic Automatic Differentiation
• Surrogate-guided optimization in quantum networks
• Bayesian Optimization for Repeater Protocols
• Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning
• Benchmarking of Quantum Protocols