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.
Highlights
Quantum metrology is an important field of quantum physics with the goal to make accurate and precise measurements of important physical quantities, ideally at a level that surpasses that achievable by any classical approach
The true acceleration that we aim to reveal by the measurements is −3 × 10−3 ωrvr, and the measurements are sampled from the momentum distribution at the end of the interferometry sequence, as shown in the inset. (b) Standard deviation of the acceleration estimated using Bayes theorem for up to 104 atoms
We demonstrate that reinforcement learning provides additional capability over traditional experimental techniques by comparing the sensitivity of the shaken lattice interferometer with the conventional Bragg interferometer
Summary
Quantum metrology is an important field of quantum physics with the goal to make accurate and precise measurements of important physical quantities, ideally at a level that surpasses that achievable by any classical approach. The usual direct-design approaches to engineering complex quantum systems that consist of many degrees of freedom or many particles are typically founded on experience with simpler analogs or intuition for underlying mechanisms This naturally leads to a paradigm that is most accessible in terms of understanding, but incorporates human bias that may potentially generate nonoptimal solutions. With this perspective in mind, we point out there are a few purely systematic methods that are often used as a way to develop unbiased strategies, including optimal control [6] and optimization algorithms such as the Nelder-Mead simplex [7] or simulated annealing [8]. V, we evaluate the resulting performance of the interferometer through a Bayesian statistical analysis
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