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]. Then, our recent progress will be demonstrated in applying ML-QST on Wigner currents [4], FPGA [5], and quantumness measure [6].
Language: English
References
Hsien-Yi Hsieh, et al., “Extract the Degradation Information in Squeezed States with Machine Learning”, Phys. Rev. Lett., 128 (2022), 073604
Hsien-Yi Hsieh, et al., “Neural-network-enhanced Fock-state tomography”, Phys. Rev. A, 110 (2024), 053705
Yi-Ru Chen, et al., “Generation of heralded optical cat states by photon addition”, Phys. Rev. A, 110 (2024), 023703
Yi-Ru Chen, et al., “Experimental reconstruction of Wigner phase-space current”, Phys. Rev. A, 108 (2023), 023729
Hsun-Chung Wu, et al., “Machine learning enhanced quantum state tomography on a field-programmable gate array”, APL Quantum, 2 (2025), 026117, Cover; Featured Article; Scilight
Ole Steuernagel and RKL, Quantumness Measure from Phase Space Distributions, arXiv: 2311.17399
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