Quantum force sensing by digital twinning of atomic Bose-Einstein condensates

Abstract

High sensitivity detection plays a vital role in science discoveries and technological applications. While intriguing methods utilizing collective many-body correlations and quantum entanglements have been developed in physics to enhance sensitivity, their practical implementation remains challenging due to rigorous technological requirements. Here, we propose an entirely data-driven approach that harnesses the capabilities of machine learning, to significantly augment weak-signal detection sensitivity. In an atomic force sensor, our method combines a digital replica of force-free data with anomaly detection technique, devoid of any prior knowledge about the physical system or assumptions regarding the sensing process. Our findings demonstrate a significant advancement in sensitivity, achieving an order of magnitude improvement over conventional protocols in detecting a weak force of approximately 10−25N. The resulting sensitivity reaches $$1.7(4)\times 1{0}^{-25}\,{{{{{{{\rm{N}}}}}}}}/\sqrt{{{{{{{{\rm{Hz}}}}}}}}}$$ . Our machine learning-based signal processing approach does not rely on system-specific details or processed signals, rendering it highly applicable to sensing technologies across various domains. In this study, the authors propose a generic machine-learning-assisted framework to improve the overall performance of quantum sensing application. In the context of an atomic force sensor, this entirely data-driven approach, which involves generating the digital twinning of experimental data, demonstrates an order of magnitude improvement in sensitivity compared to conventional protocols.