Machine-learning enhanced quantum state tomography and its applications for optical cat states

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], as an example of quantum machine learning [2]. Our recent progress will be demonstrated in applying such a ML-QST on Wigner currents [3], single-photon Fock state [4], optical cat state [5], Bayesian estimation for gravitational wave detectors [6, 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] 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).
[3] Yi-Ru Chen, et al., "Experimental reconstruction of Wigner phase-space current," Phys. Rev. A 108, 023729 (2023)
[4] Hsien-Yi Hsieh, et al., "Neural network enhanced single-photon Fock state tomography," revised to Phys. Rev. A (2024); [arXiv: 2405.02812].
[5] Yi-Ru Chen, et al., "Generation of heralded optical ‘Schroedinger cat’ states by photon-addition," Phys. Rev. A 110, 023703 (2024).
[6] Hsien-Yi Hsieh, et al., in preparation for publication (2024).
[7] 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].