Quantum State Tomography via Nonconvex Riemannian Gradient Descent

Hsu, Ming-Chien; Kuo, En-Jui; Yu, Wei-Hsuan; Cai, Jian-Feng; Hsieh, Min-Hsiu

doi:10.1103/physrevlett.132.240804

Phys. Rev. Lett.

2024

DOI: 10.1103/physrevlett.132.240804

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Quantum State Tomography via Nonconvex Riemannian Gradient Descent

Ming-Chien Hsu,

En-Jui Kuo,

Wei-Hsuan Yu

et al.

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Efficient factored gradient descent algorithm for quantum state tomography

Wang,

Liu,

Cheng

et al. 2024

Phys. Rev. Research

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Reconstructing the state of quantum many-body systems is of fundamental importance in quantum information tasks, but extremely challenging due to the curse of dimensionality. In this work, we present an efficient quantum tomography protocol that combines the state-factored with eigenvalue mapping to address the rank-deficient issue and incorporates a momentum-accelerated gradient descent algorithm to speed up the optimization process. We implement extensive numerical experiments to demonstrate that our factored gradient descent algorithm efficiently mitigates the rank-deficient problem and admits orders of magnitude better tomography accuracy and faster convergence. We also find that our method can accomplish the full-state tomography of random 11-qubit mixed states within one minute. Published by the American Physical Society 2024

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Efficient factored gradient descent algorithm for quantum state tomography

Wang,

Liu,

Cheng

et al. 2024

Phys. Rev. Research

View full text Add to dashboard Cite

show abstract

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Quantum State Tomography via Nonconvex Riemannian Gradient Descent

Cited by 1 publication

References 58 publications

Efficient factored gradient descent algorithm for quantum state tomography

Efficient factored gradient descent algorithm for quantum state tomography

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