Machine-learning enhanced quantum state tomography

By implementing machine learning architecture with a convolutional neural network, we illustrate a fast, robust, and precise quantum state tomography for continuous variables, through the experimentally measured data generated from squeezed vacuum states [1].  With the help of machine learning-enhanced quantum state tomography, we also experimentally reconstructed the Wigner’s quantum phase current for the first time [2]. Applications of squeezed states for the implementations of optical cat stats and fault-tolerant quantum computing will also be introduced.  At the same time, as a collaborator for LIGO-Virgo-KAGRA gravitational wave network and Einstein Telescope, I will introduce our plan to inject this squeezed vacuum field into the advanced gravitational wave detectors [3]. 
 

[1] Yi-Ru Chen, et al., "Experimental Reconstruction of Wigner Distribution Currents in Quantum Phase Space," [arXiv: 2111.08285].

[2] Hsien-Yi Hsieh, et al., "Extract the Degradation Information in Squeezed States with Machine Learning," [arXiv: 2106.04058].

[3] Yuhang Zhao, et al., "Frequency-dependent squeezed vacuum source for broadband quantum noise reduction in advanced gravitational-wave detectors," Phys. Rev. Lett. 124, 171101 (2020);   Editors' Suggestion; Featured in Physics