Automated design of quantum optical experiments for device-independent quantum key distribution

TL;DR

This paper presents an automated method combining reinforcement learning and optimization to design photonic experiments for device-independent quantum key distribution, achieving configurations with high key rates and robustness.

Contribution

It introduces a novel automated approach for designing quantum optical experiments specifically for DIQKD, improving feasibility and performance.

Findings

Generated experimental configurations with high key rates.

Configurations show high resistance to loss and noise.

Automated design facilitates DIQKD implementation.

Abstract

Device-independent quantum key distribution (DIQKD) reduces the vulnerability to side-channel attacks of standard QKD protocols by removing the need for characterized quantum devices. The higher security guarantees come however, at the price of a challenging implementation. Here, we tackle the question of the conception of an experiment for implementing DIQKD with photonic devices. We introduce a technique combining reinforcement learning, optimisation algorithm and a custom efficient simulation of quantum optics experiments to automate the design of photonic setups maximizing a given function of the measurement statistics. Applying the algorithm to DIQKD, we get unexpected experimental configurations leading to high key rates and to a high resistance to loss and noise. These configurations might be helpful to facilitate a first implementation of DIQKD with photonic devices and for future developments targeting improved performances.