Learning and interpreting policies for simultaneous entanglement requests in quantum networks

2026-09-24

AI Summary

This study addresses the challenge of entanglement resource scheduling under concurrent multi-task scenarios in quantum networks by proposing a reinforcement learning-based policy optimization framework. Methodologically, it integrates Message Passing Neural Networks (MPNN) with Double Deep Q-Networks (DDQN) to process dynamic graph structures, enabling entanglement request responses with low latency and minimal resource consumption. Additionally, Large Language Models (LLMs) are introduced to extract interpretable heuristic rules from the trained policies. Experimental results demonstrate that the proposed approach maintains high success rates even under significantly reduced link activation probabilities. Furthermore, the LLM-extracted heuristics achieve performance comparable to the original trained policies, effectively balancing decision-making efficiency with interpretability.

Abstract

Future quantum networks will make use of entanglement to perform numerous tasks, such as sending quantum information over long distances, distributed quantum computing, and quantum sensing. In general, these tasks will need to be performed simultaneously in various regions of a network, while minimizing resources and latency. We will thus require policies for scheduling link-level entanglement resources, and using the link-level entanglement to create various forms of multipartite entanglement required for every task. In this work, we address this problem using reinforcement learning. We formulate a Markov Decision Process for the problem and use double deep Q-networks (DQN) with Message Passing Neural Networks (MPNNs), experience replay buffers, and curriculum training to obtain policies. The key physical parameter is the probability of link-level entanglement generation, i.e., the link activation probability. We show that our policies maintain 100% success for up to 71% lower link activation probability than the baseline heuristics for a set of physically relevant network topologies. We then examine an additional constraint where experiment (task) placements are restricted to specific hardware types and demonstrate a similar advantage in performance over heuristics, with our policy maintaining at least an 80% success rate for up to a 59% lower link activation probability. Finally, we explore methods to interpret the learned policy by defining metrics enabling conclusions to be drawn about the model's behavior and by tasking a large language model (LLM) to derive a novel heuristic given example actions taken by the DQN-trained policy. We find that the LLM heuristic performs similarly to the DQN-trained policy in performance, indicating a promising method for interpretable policy extraction for large quantum networks, where direct training becomes computationally expensive.

Problem

• quantum networks
• entanglement scheduling
• multipartite entanglement
• simultaneous requests
• resource optimization

Innovation

• Reinforcement Learning
• Quantum Networks
• Message Passing Neural Networks
• Double Deep Q-Networks
• Large Language Models