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Bayesian inference of cat-state degradation via machine learning
會議論文集

Bayesian inference of cat-state degradation via machine learning

H.-Y. Hsieh, J.C. Rodríguez Pérez 和 R.-K. Lee
Proceedings of SPIE - The International Society for Optical Engineering, 卷.13563
2025
DOI: https://doi.org/10.1117/12.3071017
Web of Science ID: WOS:001715960900031

摘要

Bayesian Machine learning Quantum optics Tomography Barium compounds Diagnosis Gaussian noise (electronic) Image processing Inference engines Learning systems Machine learning Quantum efficiency Uncertainty analysis Bayesian Bayesian inference Cat state Flow based Gaussian quantum state Higher efficiency Learning frameworks Machine-learning Non-Gaussian Parameters estimation Quantum optics
We introduce a high-efficiency flow-based machine learning framework for parameter estimation, facilitating accurate diagnostics of degraded non-Gaussian quantum states. By integrating uncertainty quantification with prior information, this method improves inference robustness and enables precise quantum state reconstruction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

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