Universität zu Köln

Schmitt, Markus ORCID: 0000-0003-2223-8696 and Lenarcic, Zala ORCID: 0000-0001-8374-8011 (2022). From observations to complexity of quantum states via unsupervised learning. Phys. Rev. B, 106 (4). COLLEGE PK: AMER PHYSICAL SOC. ISSN 2469-9969

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Abstract

The vast complexity is a daunting property of generic quantum states that poses a significant challenge for theoretical treatment, especially in nonequilibrium setups. Therefore, it is vital to recognize states which are locally less complex and thus describable with (classical) effective theories. We use unsupervised learning with autoencoder neural networks to detect the local complexity of time-evolved states by determining the minimal number of parameters needed to reproduce local observations. The latter can be used as a probe of thermalization, to assign the local complexity of density matrices in open setups, and for the reconstruction of underlying Hamiltonian operators. Our approach is an ideal diagnostics tool for data obtained from (noisy) quantum simulators because it requires only practically accessible local observations.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Schmitt, Markus UNSPECIFIED orcid.org/0000-0003-2223-8696 UNSPECIFIED Lenarcic, Zala UNSPECIFIED orcid.org/0000-0001-8374-8011 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-679098
DOI:	10.1103/PhysRevB.106.L041110
Journal or Publication Title:	Phys. Rev. B
Volume:	106
Number:	4
Date:	2022
Publisher:	AMER PHYSICAL SOC
Place of Publication:	COLLEGE PK
ISSN:	2469-9969
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language PHASE-TRANSITION; DIMENSIONALITY; SIMULATION Multiple languages Materials Science, Multidisciplinary; Physics, Applied; Physics, Condensed Matter Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/67909