Hybrid reconstruction of quantum density matrix: when low-rank meets sparsity

DOI10.1007/s11128-017-1720-xzbMath1382.81054OpenAlexW2766718206MaRDI QIDQ1698802

Peng Zhang

Publication date: 16 February 2018

Published in: Quantum Information Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11128-017-1720-x

zbMATH Keywords

reconstructionfixed-point equationsparsecompressive sensinglow-rankquantum state estimation

Mathematics Subject Classification ID

Point estimation (62F10) Quantum measurement theory, state operations, state preparations (81P15) Quantum state estimation, approximate cloning (81P50)

Related Items (1)

An online optimization algorithm for the real-time quantum state tomography

Cites Work

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• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators
• Generating random density matrices
• Compressed sensing

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