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arXiv · 2211.11655

Improved Tomographic Estimates by Specialised Neural Networks

Massimiliano Guarneri·Ilaria Gianani·Marco Barbieri·Andrea Chiuri
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Abstract

Characterization of quantum objects, being them states, processes, or measurements, complemented by previous knowledge about them is a valuable approach, especially as it leads to routine procedures for real-life components. To this end, Machine Learning algorithms have demonstrated to successfully operate in presence of noise, especially for estimating specific physical parameters. Here we show that a neural network (NN) can improve the tomographic estimate of parameters by including a convolutional stage. We applied our technique to quantum process tomography for the characterization of several quantum channels. We demonstrate that a stable and reliable operation is achievable by training the network only with simulated data. The obtained results show the viability of this approach as an effective tool based on a completely new paradigm for the employment of NNs operating on classical data produced by quantum systems.

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Massimiliano Guarneri, Ilaria Gianani, Marco Barbieri, Andrea Chiuri. 2022-11-21. Improved Tomographic Estimates by Specialised Neural Networks. https://doi.org/10.1002/qute.202300027

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