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  4. Scalable approach to many-body localization via quantum data
 
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Scalable approach to many-body localization via quantum data

Publikationstyp
Preprint
Date Issued
2022-02-17
Sprache
English
Author(s)
Gresch, Alexander  
Bittel, Lennart  
Kliesch, Martin  
TORE-URI
http://hdl.handle.net/11420/14162
Citation
arXiv: 2202.08853 (2022)
Publisher DOI
10.48550/arXiv.2202.08853
ArXiv ID
2202.08853v1
We are interested in how quantum data can allow for practical solutions to otherwise difficult computational problems. A notoriously difficult phenomenon from quantum many-body physics is the emergence of many-body localization (MBL). So far, is has evaded a comprehensive analysis. In particular, numerical studies are challenged by the exponential growth of the Hilbert space dimension. As many of these studies rely on exact diagonalization of the system's Hamiltonian, only small system sizes are accessible. In this work, we propose a highly flexible neural network based learning approach that, once given training data, circumvents any computationally expensive step. In this way, we can efficiently estimate common indicators of MBL such as the adjacent gap ratio or entropic quantities. Our estimator can be trained on data from various system sizes at once which grants the ability to extrapolate from smaller to larger ones. Moreover, using transfer learning we show that already a two-dimensional feature vector is sufficient to obtain several different indicators at various energy densities at once. We hope that our approach can be applied to large-scale quantum experiments to provide new insights into quantum many-body physics.
Subjects
Physics - Disordered Systems and Neural Networks
Physics - Disordered Systems and Neural Networks
Computer Science - Learning
Quantum Physics
MLE@TUHH
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