On the experimental feasibility of quantum state reconstruction via machine learning

• URL: http://arxiv.org/abs/2012.09432v1
• Date: Thu, 17 Dec 2020 07:51:47 GMT
• Title: On the experimental feasibility of quantum state reconstruction via machine learning
• Authors: Sanjaya Lohani, Thomas A. Searles, Brian T. Kirby, and Ryan T. Glasser
• Abstract summary: We determine the resource scaling of machine learning-based quantum state reconstruction methods for systems of up to four qubits. We examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. We implement our quantum state reconstruction method on a IBM Q quantum computer and confirm our results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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