Deep reinforcement learning for quantum multiparameter estimation
This work addresses the challenge of efficient quantum metrology for scientific research by reducing computational and experimental demands, though it is incremental as it builds on existing Bayesian adaptive estimation with AI enhancements.
The authors tackled the problem of quantum multiparameter estimation by introducing a model-free deep learning approach that combines neural networks and reinforcement learning to optimize resource allocation without prior system knowledge, achieving higher estimation performances than standard methods in experiments on an integrated photonic circuit.
Estimation of physical quantities is at the core of most scientific research and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that the resources are limited and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, during the estimation process, all the available resources. However, this framework relies on the precise knowledge of the system model, retrieved with a fine calibration that often results computationally and experimentally demanding. Here, we introduce a model-free and deep learning-based approach to efficiently implement realistic Bayesian quantum metrology tasks accomplishing all the relevant challenges, without relying on any a-priori knowledge on the system. To overcome this need, a neural network is trained directly on experimental data to learn the multiparameter Bayesian update. Then, the system is set at its optimal working point through feedbacks provided by a reinforcement learning algorithm trained to reconstruct and enhance experiment heuristics of the investigated quantum sensor. Notably, we prove experimentally the achievement of higher estimation performances than standard methods, demonstrating the strength of the combination of these two black-box algorithms on an integrated photonic circuit. This work represents an important step towards fully artificial intelligence-based quantum metrology.