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  4. Riemannian approach to the Lindbladian dynamics of a locally purified tensor network
 
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Riemannian approach to the Lindbladian dynamics of a locally purified tensor network

Citation Link: https://doi.org/10.15480/882.16248
Publikationstyp
Journal Article
Date Issued
2025-07-28
Sprache
English
Author(s)
Godinez Ramirez, Emiliano  
Quantum Inspired and Quantum Optimization E-25  
Milbradt, Richard M.  
Mendl, Christian B.  
TORE-DOI
10.15480/882.16248
TORE-URI
https://hdl.handle.net/11420/59097
Journal
Physical Review A / Atomic, molecular, and optical physics  
Volume
112
Issue
1
Article Number
012224
Citation
Physical Review A 112 (1): 012224 (2025)
Publisher DOI
10.1103/kztw-ywxr
Scopus ID
2-s2.0-105022111331
Publisher
American Physical Society
Tensor networks offer a valuable framework for implementing Lindbladian dynamics in many-body open quantum systems with nearest-neighbor couplings. In particular, a tensor network Ansatz known as the locally purified density operator employs the local purification of the density matrix to guarantee the positivity of the state at all times.Within this framework, the dissipative evolution utilizes the Trotter-Suzuki splitting, yielding a second-order approximation error. However, due to the Lindbladian dynamics’ nature, employing higher-order schemes results in nonphysical quantum channels. In this work, we leverage the gauge freedom inherent in the Kraus representation of quantum channels to improve the splitting error. To this end, we formulate an optimization problem on the Riemannian manifold of isometries and find a solution via the second-order trust-region algorithm. We validate our approach using two nearest-neighbor noise models and achieve an improvement of orders of magnitude compared to other positivity-preserving schemes. In addition, we demonstrate the usefulness of our method as a compression scheme, helping to control the exponential growth of computational resources, which thus far has limited the use of the locally purified Ansatz.
DDC Class
530: Physics
519: Applied Mathematics, Probabilities
Lizenz
https://creativecommons.org/licenses/by/4.0/
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