Beecroft Building, Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PU
Dr Ray-Kuang Lee, National Tsing Hua University, Taiwan
Abstract
With this talk, I will first illustrate the implementation of our machine-learning (ML) enhanced quantum state tomography (QST) for continuous variables, through the experimentally measured data generated from squeezed vacuum states [1], single-photon Fock states [2], and optical cat states [3], as an example of quantum machine learning [4]. Our recent progress will be demonstrated in applying such a ML-QST on Wigner currents [5], FPGA [6], Bayesian estimation for gravitational wave detectors [7], and quantumness measure [8].
[1] Hsien-Yi Hsieh, et al., "Extract the Degradation Information in Squeezed States with Machine Learning," Phys. Rev. Lett. 128, 073604 (2022).
[2] Hsien-Yi Hsieh, et al., "Neural-network-enhanced Fock-state tomography," Phys. Rev. A 110, 053705 (2024).
[3] Yi-Ru Chen, et al., "Generation of heralded optical `Schroedinger cat' states by photon-addition," Phys. Rev. A 110, 023703 (2024)
[4] Alexey Melnikov, Mohammad Kordzanganeh, Alexander Alodjants, and RKL," Quantum Machine Learning: from physics to software engineering," Adv. in Phys. X [Review Article) 8, 2165452 (2023).
[5] Yi-Ru Chen, et al., "Experimental reconstruction of Wigner phase-space current," Phys. Rev. A 108, 023729 (2023).
[6] Hsun-Chung Wu, et al., "Machine-learning-enhanced quantum state tomography on a field-programmable gate array," [arXiv: 2501.04327].
[6] 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.
[8] Ole Steuernagel and RKL, "Quantumness Measure from Phase Space Distributions," [arXiv: 2311.17399].