Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation

Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as number probes, their coherence time, or nonclassical quantum states. The adaptive Bayesian approach to parameter estimation allows an efficient use because experiment design. For its practical success, fast numerical solutions for update and design are crucial. Here we show that neural networks can be trained become strong experiment-design heuristics using a combination evolutionary strategy reinforcement learning. Neural-network shown outperform established technologically important example frequency qubit dephasing. Our method creating neural-network is very general complements well-studied sequential Monte Carlo updates form complete framework estimation.Received 27 May 2020Accepted 17 March 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020303Published by American Physical Society under terms Creative Commons Attribution 4.0 International license. Further distribution this work must maintain attribution author(s) published article's title, journal citation, DOI.Published SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum controlQuantum metrologyQuantum sensingQuantum Information