Deep reinforcement learning for key distribution based on quantum repeaters

TL;DR

This paper applies deep reinforcement learning to optimize memory storage time strategies in quantum repeaters, significantly improving secret key rates in quantum key distribution over long distances.

Contribution

It introduces a novel deep reinforcement learning approach for dynamic memory cut-off strategies in quantum repeaters, outperforming naive methods.

Findings

Reinforcement learning strategies outperform naive fixed cut-offs.

Dynamic strategies adapt better to quantum state evolution.

Proof of concept with four-segment quantum repeaters achieved.

Abstract

This work examines secret key rates of key distribution based on quantum repeaters in a broad parameter space of the communication distance and coherence time of the quantum memories. As the first step in this task, a Markov decision process modeling the distribution of entangled quantum states via quantum repeaters is developed. Based on this model, a simulation is implemented, which is employed to determine secret key rates under naively controlled, limited memory storage times for a wide range of parameters. The complexity of the quantum state evolution in a multiple-segment quantum repeater chain motivates the use of deep reinforcement learning to search for optimal solutions for the memory storage time limits - the so-called memory cut-offs. The novel contribution in this work is to explore very general cut-off strategies which dynamically adapt to the state of the quantum repeater. An implementation of this approach is presented, with a particular focus on four-segment quantum repeaters, achieving proof of concept of its validity by finding exemplary solutions that outperform the naive strategies.