Speaker
Description
We develop a normalizing flow-based Bayesian parameter estimation (BPE) framework to characterize noisy quantum states and uncover correlations among physical parameters. By incorporating physically interpretable parameters into quantum state ansatzes, our approach infers their posterior distributions directly from experimental data, enabling uncertainty quantification and correlation analysis. We demonstrate the framework using two physical ansatzes for optical cat-state experiments, where noisy single-photon states are added to impure squeezed states. By examining correlations among Wigner function negativity, photon-addition fraction, noise contributions, and pump powers, we gain insights into the experimental mechanisms underlying the generation of noisy non-Gaussian states. Combining Bayesian inference with physical and experimental constraints, our framework provides interpretable parameter estimates and practical guidance for optimizing desirable quantum features.