Preprint

PUBDB-2025-04648

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects

Bulgarelli, A. (Corresponding author)INFN* ; Cellini, E. ; Jansen, K.DESY* ; Kühn, S.DESY* ; Nada, A.DESY* ; Nakajima, S. ; Nicoli, K. A. ; Panero, M.

2025

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Please use a persistent id in citations: doi:10.3204/PUBDB-2025-04648

Report No.: arXiv:2410.14466

Abstract: We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the ϕ4 scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size.

Note: Phys. Rev. Lett. 134, 151601 (2025). some discussions improved, matches the published version

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Contributing Institute(s):

1. Centre f. Quantum Techno. a. Application (CQTA)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)
2. QUEST - QUantum computing for Excellence in Science and Technology (101087126) (101087126)
3. DFG project G:(GEPRIS)511713970 - SFB 1639: NuMeriQS: Numerische Methoden zur Untersuchung von Dynamik und Strukturbildung in Quantensystemen (511713970) (511713970)
4. AQTIVATE - Advanced computing, quantum algorithms, and data-driven approaches for science, technology and engineering (101072344) (101072344)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2025

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Database coverage:
; ; Published

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Bulgarelli, A. (Corresponding author)INFN* ; Cellini, E. ; Jansen, K.DESY* ; Kühn, S. (Corresponding author)DESY* ; Nada, A. ; Nakajima, S. ; Nicoli, K. A. ; Panero, M.
Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects
Physical review letters 134(15), 151601 (2025) [10.1103/PhysRevLett.134.151601]2025     Files  Fulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS

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