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quantum state tomography

topic2 events
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.

quantum/optimization/tensor-decomposition/low-rank
papersSEP 10 04:00 UTC

Optimal sample complexity for low-rank quantum state tomography with joint measurements

A new paper determines the optimal sample complexity for estimating an unknown low-rank quantum state when each measurement can act jointly on at most t copies. The results characterize how the state's rank and dimension shape the number of samples needed to reach a target error in this bounded-measurement setting.

quantum/research/tomography/sample-complexity