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.
@article{arxiv.2211.11655,
title = {Improved Tomographic Estimates by Specialised Neural Networks},
author = {Massimiliano Guarneri and Ilaria Gianani and Marco Barbieri and Andrea Chiuri},
journal= {arXiv preprint arXiv:2211.11655},
year = {2023}
}