Artificial neural networks bridge input data into output results by approximately encoding the function that relates them. This is achieved after training the network with a collection of known inputs and results leading to an adjustment of the neuron connections and biases. In the context of quantum detection schemes, neural networks find a natural playground. In particular, in the presence of a target, a quantum sensor delivers a response, i.e., the input data, which can be subsequently processed by a neural network that outputs the target features. We demonstrate that adequately trained neural networks enable to characterize a target with minimal knowledge of the underlying physical model, in regimes where the quantum sensor presents complex responses, and under a significant shot noise due to a reduced number of measurements. We exemplify the method with a development for 171Yb+ atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.
@article{arxiv.2012.07677,
title = {Neural-network-based parameter estimation for quantum detection},
author = {Yue Ban and Javier Echanobe and Yongcheng Ding and Ricardo Puebla and Jorge Casanova},
journal= {arXiv preprint arXiv:2012.07677},
year = {2021}
}