Phase diagram detection via Gaussian fitting of number probability distribution

Year: 2023

Authors: Contessi D., Recati A., Rizzi M.

Autors Affiliation: Univ Trento, Dipartimento Fis, I-38123 Povo, Italy; INO CNR BEC Ctr, I-38123 Povo, Italy; Forschungszentrum Julich GmbH, Inst Quantum Control, Peter Grunberg Inst PGI 8, D-52425 Julich, Germany; Univ Cologne, Inst Theoret Phys, D-50937 Cologne, Germany.

Abstract: We investigate the number probability density function that characterizes subportions of a quantum many-body system with globally conserved number of particles. We put forward a linear fitting protocol capable of mapping out the ground-state phase diagram of the rich one-dimensional extended Bose-Hubbard model: The results are quantitatively comparable with more sophisticated traditional and machine learning techniques. We argue that the studied quantity should be considered among the most informative bipartite properties, being moreover readily accessible in atomic gases experiments.

Journal/Review: PHYSICAL REVIEW B

Volume: 107 (12)      Pages from: L121403-1  to: L121403-6

More Information: We acknowledge support from the Deutsche Forschungs-gemeinschaft (DFG), Grant No. 277101999, within the CRC network TR 183 (subproject B01); the Alexander von Humboldt Foundation; the Provincia Autonoma di Trento, from Q@TN (the joint laboratory between University of Trento, FBK-Fondazione Bruno Kessler, INFN-National Institute for Nuclear Physics, and CNR-National Research Council); and from the Italian MIUR under the PRIN2017 project CEnTraL. We gratefully acknowledge discussions with M.Kiefer-Emmanouilidis and A. Haller. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. [47]for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on theGCS Supercomputer JUWELS (Grant NeTeNeSyQuMa) and JURECA (institute project PGI-8) at Juelich Supercomputing Centre (JSC). The MPS simulations were run with a codebased on a flexible Abelian symmetric tensor networks library, developed in collaboration with the group of S. Montangero(Padua)
KeyWords: Entanglement Entropy
DOI: 10.1103/PhysRevB.107.L121403

ImpactFactor: 3.200
Citations: 3
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