Quantum network policies cut resource needs and improve success rates
Learning and interpreting policies for simultaneous entanglement requests in quantum networks
Machine Learning
Summary
Quantum networks use a special kind of connection called entanglement to do important jobs like sending information and shared computing. The authors studied how to best manage these connections so many jobs can happen at once without wasting resources or causing delays. They created smart computer programs that learn good scheduling policies and work better than simple rules, especially when conditions get harder. They also found ways to explain how their programs decide what to do, making the technology easier to understand and trust.
What this means in practice
- •For quantum network operators: Manage entanglement links efficiently to support simultaneous quantum tasks with fewer resources under variable network conditions.
- •For distributed computing teams: Schedule quantum connections to improve the reliability of distributed quantum computing across hardware-constrained environments.
Authors
Leon Rode, Sumeet Khatri, Supartha Podder
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.