papersSEP 10 04:00 UTC
Block Tensor Train Burer-Monteiro Framework Proposed for Low-Rank Quantum State Tomography
A new preprint presents an optimization framework that pairs block tensor train decompositions with the Burer-Monteiro approach to make low-rank quantum state tomography more computationally tractable. Reconstructing quantum states from measurement data is essential for evaluating quantum devices, but conventional estimators scale poorly. The work appeared as a cross-listed arXiv paper in the machine learning category.
arXivBurer-Monteiro factorizationblock tensor train decompositionlow-rank quantum state tomographymachine-learningquantum state tomography
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arXiv cs.LGA Block Tensor Train Burer-Monteiro Framework for Low-Rank Quantum State Tomography ↗SEP 10 04:00 UTC