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On the experimental feasibility of quantum state reconstruction via machine learning

2020-12-17 07:51:47
Sanjaya Lohani, Thomas A. Searles, Brian T. Kirby, Ryan T. Glasser

Abstract

We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of both inference and training, for systems of up to four qubits. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state reconstruction method on a IBM Q quantum computer and confirm our results.

Abstract (translated)

URL

https://arxiv.org/abs/2012.09432

PDF

https://arxiv.org/pdf/2012.09432.pdf


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