Quantum Metrology Assisted by Machine Learning

Quantum 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. In recent, there appears a hectic development in the field of machine learning, with applications now touching every sector of quantum technologies. With a focus on optimizing the key metrology stages for better measurement precision, this review illustrates the fundamental principles, potential applications, and recent advancements in quantum metrology assisted by machine learning.image