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, as an example of quantum machine learning. Our recent progress in applying such a ML-QST as a crucial diagnostic toolbox for applications with squeezed states, from Wigner currents, optical cat state generation, and Bayesian estimation for gravitational wave detectors will be reported.
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[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 cat states by photon addition," Phys. Rev. A 110, 023703 (2024)
[4] Yi-Ru Chen, et al., "Experimental reconstruction of Wigner phase-space current," Phys. Rev. A 108, 023729 (2023).
[5] Hsun-Chung Wu, et al., "Machine learning enhanced quantum state tomography on a field-programmable gate array," APL Quantum 2, 026117 (2025); Cover; Featured Article; Scilight.
[6] Ole Steuernagel and RKL, "Quantumness Measure from Phase Space Distributions," [arXiv: 2311.17399].