Machine-Learning enhanced Quantum State Tomography
演講者 : 李瑞光 教授 (國立清華大學電機系)
演講地點 : 理學教學新大樓物理系1F 36169教室
演講時間 : 2022 / 09 / 26 12:10
In this talk, I shall be covering fundamental details about machine-learning (ML) enhanced quantum state tomography (QST) for squeezed states. Implementation of machine learning architecture with a convolutional neural network will be illustrated and demonstrated through the experimentally measured data generated from squeezed vacuum states . In addition to using the reconstruction model in training a truncated density matrix, we also develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly . With the help of machine learning-enhanced quantum state tomography, we also experimentally reconstructed the Wigner’s quantum phase current for the first time . A brief view on quantum ML will also be discussed . 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 . I will also cover progress in applying such a ML- QST as a crucial diagnostic toolbox for applications with squeezed states, from quantum information process, quantum metrology, and macroscopic quantum state generation.