Optimal entanglement distribution policies in homogeneous repeater chains with cutoffs

Abstract

We study the limits of bipartite entanglement distribution using a chain of quantum repeaters that have quantum memories. To generate end-to-end entanglement, each node can attempt the generation of an entangled link with a neighbor, or perform an entanglement swapping measurement. A maximum storage time, known as cutoff, is enforced on the memories to ensure high-quality entanglement. Nodes follow a policy that determines when to perform each operation. Global-knowledge policies take into account all the information about the entanglement already produced. Here, we find global-knowledge policies that minimize the expected time to produce end-to-end entanglement. Our methods are based on Markov decision processes and value and policy iteration. We compare optimal policies to a policy in which nodes only use local information. We find that the advantage in expected delivery time provided by an optimal global-knowledge policy increases with increasing number of nodes and decreasing probability of successful swapping. Our work sheds light on how to distribute entangled pairs in large quantum networks using a chain of intermediate repeaters with cutoffs.

9 pages, 8 figures, 15 pages appendix with 10 figures

References in corpus (5)

• Free-Space distribution of entanglement and single photons over 144 km
• Realization of a multi-node quantum network of remote solid-state qubits
• Multiplexed Memory-Insensitive Quantum Repeaters
• Optimal approach to quantum communication using dynamic programming
• On the design and analysis of near-term quantum network protocols using Markov decision processes

Cited by in corpus (17)

• Fast and reliable entanglement distribution with quantum repeaters: principles for improving protocols using reinforcement learning
• Pre-Distribution of Entanglements in Quantum Networks
• Performance metrics for the continuous distribution of entanglement in multi-user quantum networks
• Entanglement Distribution in the Quantum Internet: Knowing when to Stop!
• ReQuSim: Faithfully simulating near-term quantum repeaters
• Aging and Reliability of Quantum Networks
• Reducing classical communication costs in multiplexed quantum repeaters using hardware-aware quasi-local policies
• Entanglement buffering with two quantum memories
• On noise in swap ASAP repeater chains: exact analytics, distributions and tight approximations
• Asymmetric node placement in fiber-based quantum networks
• Continuously Distributing Entanglement in Quantum Networks with Regular Topologies
• Optimization of Quantum-Repeater Networks using Stochastic Automatic Differentiation
• Fidelity-Aware Multipath Routing for Multipartite State Distribution in Quantum Networks
• Bayesian Optimization for Repeater Protocols
• Surrogate-guided optimization in quantum networks
• Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning
• RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks