Colloquium
Department of Physics, NCU
Machine-Learning enhanced Quantum State Tomography
Prof. Ray-Kuang Lee (李瑞光)
Institute of Photonics Technologies (IPT), NTHU
Date 2022.05.17 (Tue)
Place S4-625
Time 14:00-16:00
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Abstract :
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] Hsien-Yi Hsieh, et al., “Extract the Degradation Information in Squeezed States with Machine Learning,” Phys. Rev. Lett. 128, 073604 (2022).
[2] Yi-Ru Chen, et al., “Experimental Reconstruction of Wigner Distribution Currents in Quantum Phase Space,” [arXiv: 2111.08285].
[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