Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology

Understand

Quantum sensors offer control flexibility during estimation by allowing manipulation by the experimenter across various parameters.

• For each sensing platform, pinpointing the optimal controls to enhance the sensor's precision remains a challenging task.
• While an analytical solution might be out of reach, machine learning offers a promising avenue for many systems of interest, especially given the capabilities of contemporary hardware.
• We have introduced a versatile procedure capable of optimizing a wide range of problems in quantum metrology, estimation, and hypothesis testing by combining model-aware reinforcement learning (RL) with Bayesian estimation based on particle filtering.