Machine Learning Enhanced Quantum State Tomography: A Covariance Matrix Approach

We present a machine learning-based approach for efficient quantum state tomography, reconstructing covariance matrices of single- and two-mode squeezed states from homo dyne data. This method ensures precise, real-time quantum property estimation and noise robustness.