Affiliation:
1. Institute of Quantum Precision Measurement, State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen 518060 China
2. College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China
3. Laboratory of Quantum Engineering and Quantum Metrology School of Physics and Astronomy Sun Yat‐Sen University (Zhuhai Campus) Zhuhai 519082 China
4. Quantum Science Center of Guangdong‐Hongkong‐Macao Greater Bay Area (Guangdong) Shenzhen 518045 China
Abstract
AbstractQuantum metrology aims to measure physical quantities based on fundamental quantum principles, enhancing measurement precision through resources like quantum entanglement and quantum correlations. This field holds promise for advancing quantum‐enhanced sensors, including atomic clocks and magnetometers. However, practical constraints exist in the four fundamental steps of quantum metrology, including initialization, sensing, readout, and estimation. Valuable resources, such as coherence time, impose limitations on the performance of quantum sensors. Machine learning, enabling learning and prediction without explicit knowledge, provides a powerful tool in optimizing quantum metrology with limited resources. This article reviews the fundamental principles, potential applications, and recent advancements in quantum metrology assisted by machine learning.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Computational Theory and Mathematics,Condensed Matter Physics,Mathematical Physics,Nuclear and High Energy Physics,Electronic, Optical and Magnetic Materials,Statistical and Nonlinear Physics