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 homodyne data. This method ensures precise, real-time quantum property estimation and noise robustness.
Authors
- Ray‐Kuang Lee (ORCID: https://orcid.org/0000-0002-7171-7274)
- Hua Li Chen
- Ole Steuernagel (ORCID: https://orcid.org/0000-0001-6089-7022)
- Hsien-Yi Hsieh (ORCID: https://orcid.org/0000-0001-5227-8248)
- Juan Carlos Rodríguez‐Aldama (ORCID: https://orcid.org/0000-0001-8664-9879)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/cleo_fs.2025.ff113_5
- Primary Topic
- Quantum Information and Cryptography
- Type
- article
- Field-Weighted Citation Impact
- 0.00