Machine Learning Enhanced Bayesian Quantum State Tomography for Inferring Degradation in Optical Cat States

We propose a high-efficiency flow-based machine learning method for parameter estimation, enabling precise diagnostics of degraded non-Gaussian quantum states. This approach combines uncertainty quantification and prior knowledge to enhance inference reliability and reconstruct quantum states accurately.