Bayesian inference of cat-state degradation via machine learning

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.