Neural-network-based parameter estimation for quantum detection

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.

arXiv comment18 pages, 7 figures

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Cited by in corpus (9)

• Data-Centric Machine Learning in Quantum Information Science
• A Neural Network Assisted ${}^{171}$Yb${}^{+}$ Quantum Magnetometer
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
• Applications of model-aware reinforcement learning in Bayesian quantum metrology
• Multiparameter estimation of continuous-time Quantum Walk Hamiltonians through Machine Learning
• Quantum neural networks with multi-qubit potentials
• Automatic Detection of Nuclear Spins at Arbitrary Magnetic Fields via Signal-to-Image AI Model
• Optimizing quantum sensing networks via genetic algorithms and deep learning
• Neural Network Based Qubit Environment Characterization