Neural-network-based parameter estimation for quantum detection

• URL: http://arxiv.org/abs/2012.07677v2
• Date: Thu, 12 Aug 2021 07:52:41 GMT
• Title: Neural-network-based parameter estimation for quantum detection
• Authors: Yue Ban, Javier Echanobe, Yongcheng Ding, Ricardo Puebla, and Jorge Casanova
• Abstract summary: In the context of quantum detection schemes, neural networks find a natural playground. We demonstrate that adequately trained neural networks enable to characterize a target with minimal knowledge of the underlying physical model. We exemplify the method with a development for $171$Yb$+$ atomic sensors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $^{171}$Yb$^{+}$ atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.

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