[NCTS Physics Research Highlights] Ray-Kuang Lee 'Extract the Degradation Information in Squeezed States with Machine Learning', Physical Review Letters (2022)
Extract the Degradation Information in Squeezed States with Machine Learning
Hsien-Yi Hsieh, Yi-Ru Chen, Hsun-Chung Wu, Hua Li Chen, Jingyu Ning, Yao-Chin Huang, Chien-Ming Wu, and Ray-Kuang Lee*
Phys. Rev. Lett. 128, 073604 (2022)
In order to leverage the full power of quantum noise squeezing with unavoidable decoherence, a complete understanding of the degradation in the purity of squeezed light is demanded. By implementing machine learning architecture with a convolutional neural network, we illustrate a fast (less than one second), robust (to the thermal noises), and precise (keeping the fidelity as high as 0.99) quantum state tomography for continuous variables, through the experimentally measured data generated from the balanced homodyne detectors. Our neural network enhanced quantum state tomography provides the metrics to give physical descriptions of every feature observed in the quantum state with a single-scan measurement and paves a way of exploring large-scale quantum systems in real-time.