IEEE Conference on Decision and Control (CDC)

On how neural networks enhance quantum state tomography with limited resources

Hailan Ma, Daoyi Dong, Ian R. Petersen

Published Dec 14, 2021• 11 citations

Abstract

Quantum state tomography is defined as a process of reconstructing the density matrix of a quantum state and is an important task for various emerging quantum technologies. In this work, we propose a general quantum state tomography framework that employs deep neural networks to reconstruct quantum states from a set of measurements with high efficiency. In particular, we apply it to two cases, including few measurement copies and incomplete measurement. Numerical results demonstrate that the proposed method exhibits a significant potential to achieve high fidelity for quantum state tomography when measurement resources are limited.

DOI: 10.1109/cdc45484.2021.9683315

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