Bayesian optimization enhanced magnetic field gradient detection based on spin-orbit coupled Bose-Einstein condensates

Abstract

Magnetic field detection is of great significance in modern fields such as medical diagnostics, geological exploration, inertial navigation, and space physics. We have previously investigated the expansion dynamics of a spin-orbit-coupled Bose-Einstein condensate. Building on these results, here we propose a scheme for magnetic-field-gradient detection by establishing a quantitative relationship between the condensate's rotational deflection angle and the magnetic-field gradient. In this work, we present the parameter dependence of this scheme and perform a systematic optimization. We introduce a Bayesian optimization algorithm to automatically and efficiently search for the optimal combination of key parameters, thereby significantly enhancing the sensitivity of the magnetic field gradient measurement. The results show that the optimized gradiometer improves the sensitivity by a factor of five. This work not only provides a new approach for magnetic field gradient measurement but also serves as a reference for machine learning-empowered quantum precision measurement.