Machine Learning for Data Reduction in Quantum State Tomography
Ximin Liu, Sicong Lu, Re-Bing Wu
The purpose of quantum state tomography (QST) is to obtain a complete quantum state by reconstructing a density matrix from experimental data and therefore gives researchers a powerful tool to analyze complex synthetic quantum systems. In the past decades, many methods have been developed to improve the efficiency of quantum state tomography (QST), such as old methods: compress sensing on almost pure state and low-rank systems, new method: using restricted Boltzmann machine on systems of quantum many-body. However, they are both limited to some certain systems. In this paper, we introduce an universal method using deep neural networks based on stacked denoising autoencoder, which probably can help finish QST on all types of systems with a much smaller number of measurements and therefore is very efficient.
Chinese Control Conference (CCC)•Jul 1, 2018•0 citations