A unified structured factorization framework for quantum state tomography that parametrizes the density matrix as FF^dagger, supports multiple priors, provides sample complexity bounds, and introduces projected gradient descent and power-method algorithms.
Quantum state tomography with tensor train cross approximation.arXiv preprint arXiv:2207.06397, 2022
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A survey of structured quantum state tomography covering compact representations, measurement design, and optimization algorithms, connected to compressive sensing for sample efficiency.
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Structured Factorization Approaches for Quantum State Tomography
A unified structured factorization framework for quantum state tomography that parametrizes the density matrix as FF^dagger, supports multiple priors, provides sample complexity bounds, and introduces projected gradient descent and power-method algorithms.
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Statistical and Algorithmic Foundations of Probing Quantum Systems with Compressive Measurements: A Review
A survey of structured quantum state tomography covering compact representations, measurement design, and optimization algorithms, connected to compressive sensing for sample efficiency.