Physics

Efficient quantum state tomography with convolutional neural networks

T. Schmale, M. Reh, et al.

This innovative research introduces a quantum state tomography scheme leveraging convolutional neural networks (CNNs) to enhance the accuracy of measurement outcomes. Conducted by Tobias Schmale, Moritz Reh, and Martin Gärttner, this method significantly surpasses traditional techniques in fidelity and reduces estimation errors, making state reconstruction more efficient.

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Abstract

This paper introduces a quantum state tomography scheme using convolutional neural networks (CNNs) to approximate the probability distribution of informationally complete measurement outcomes. The method achieves high classical fidelities, outperforming standard maximum likelihood estimation (MLE), and reduces observable estimation errors by up to an order of magnitude. The number of variational parameters scales polynomially with system size, enabling efficient reconstruction of states.

Publisher

npj Quantum Information

Published On

Sep 23, 2022

Authors

Tobias Schmale, Moritz Reh, Martin Gärttner