Journal Article PUBDB-2024-07588

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Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects

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2025
APS College Park, Md.

Physical review letters 134(15), 151601 () [10.1103/PhysRevLett.134.151601]
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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 $\phi^4$ 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.

Classification:

Note: 10 pages, 9 figures

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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Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
[10.3204/PUBDB-2025-04648]  GO OpenAccess  Download fulltext Files  Download fulltextFulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS


 Record created 2024-12-11, last modified 2025-11-19