This method reconstructs covariance matrices from homodyne data, highlighting improved quantum property estimation and real-time processing.
We present a machine learning-based approach for efficient quantum state tomography, reconstructing covariance matrices of single- and two-mode squeezed states from homodyne data. This method ensures precise, real-time quantum property estimation and noise robustness.
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Rodríguez‐Aldama et al. (2025) studied this question.
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