Neural-network-based parameter estimation for quantum detection

Yue Ban

doi:10.1088/2058-9565/ac16ed

Neural-network-based parameter estimation for quantum detection

2021-08-10

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DOI：10.1088/2058-9565/ac16ed

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Artificial neural networks (NNs) 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, NNs find a natural playground. In particular, in the presence of a target (e.g. an electromagnetic field), a quantum sensor delivers a response, i.e. the input data, which can be subsequently processed by a NN that outputs the target features. In this work we demonstrate that adequately trained NNs enable to characterize a target with (i) minimal knowledge of the underlying physical model (ii) in regimes where the quantum sensor presents complex responses and (iii) under a significant shot noise due to a reduced number of measurements. We exemplify the method with a development for Yb-171(+) atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.

Keywords:

quantum detection

atomic-size quantum sensor

quantum parameter estimation

quantum magnetometry

neural network

Neural-network-based parameter estimation for quantum detection

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