Reinforcement-learning based matterwave interferometer in a shaken optical lattice

TL;DR

This paper introduces a novel matterwave interferometer design utilizing reinforcement learning to optimize optical lattice components, achieving higher acceleration sensitivity than traditional methods, with potential for advanced quantum sensing.

Contribution

The work pioneers the use of reinforcement learning to design optical lattice components for matterwave interferometry, surpassing standard sensitivity benchmarks.

Findings

Reinforcement learning effectively designs optical lattice components.

The interferometer exceeds standard Bragg interferometry sensitivity.

Bayesian analysis confirms high precision in acceleration measurement.

Abstract

We demonstrate the design of a matterwave interferometer to measure acceleration in one dimension with high precision. The system we base this on consists of ultracold atoms in an optical lattice potential created by interfering laser beams. Our approach uses reinforcement learning, a branch of machine learning, that generates the protocols needed to realize lattice-based analogs of optical components including a beam splitter, a mirror, and a recombiner. The performance of these components is evaluated by comparison with their optical analogs. The interferometer's sensitivity to acceleration is quantitatively evaluated using a Bayesian statistical approach. We find the sensitivity to surpass that of standard Bragg interferometry, demonstrating the future potential for this design methodology.