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Given a renormalization scheme, we show how to formulate a tractable convex relaxation of the set of feasible local density matrices of a many-body quantum system.
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S. Gharibian and O. Parekh, Almost Optimal Classical Approximation Algorithms for a Quantum Generalization of Max-Cut , in Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019) , Leibniz International Proceedings in Informatics (LIPIcs), Vol. 145, edited by D. Achlioptas and L. A. Végh (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany, 2019) pp. 31:1–31:17
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A. Broadbent and A. B. Grilo, QMA-hardness of Consistency of Local Density Matrices with Applications to Quantum Zero-Knowledge , in 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS) (2020) pp. 196–205
2020
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2020
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F. Baccari, C. Gogolin, P. Wittek, and A. Acín, Verifying the output of quantum optimizers with ground-state energy lower bounds , Phys. Rev. Res. 2
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M. Navascués, S. Singh, and A. Acín, Connector Tensor Networks: A Renormalization-Type Approach to Quantum Certification , Phys. Rev. X 10
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2021
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2021
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J. I. Cirac, D. Pérez-García, N. Schuch, and F. Verstraete, Matrix product states and projected entangled pair states: Concepts, symmetries, theorems , Rev. Mod. Phys. 93
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2021
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2021
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