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.

本研究提出了一种基于深度学习和强化学习的模型无关方法，实现了实际的贝叶斯量子计量任务，并在一个集成光电电路上证明了比标准方法更高的测量性能，这是实现完全基于人工智能的量子计量的重要一步。