Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
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 ϕ^{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.
Authors
- Alessandro Nada (ORCID: https://orcid.org/0000-0002-1766-5186)
- Kim A. Nicoli (ORCID: https://orcid.org/0000-0001-5933-1822)
- Shinichi Nakajima (ORCID: https://orcid.org/0000-0003-3970-4569)
- Marco Panero (ORCID: https://orcid.org/0000-0001-9477-3749)
- Elia Cellini (ORCID: https://orcid.org/0000-0002-5664-9752)
- Stefan Kühn (ORCID: https://orcid.org/0000-0001-7693-350X)
- Karl Jansen (ORCID: https://orcid.org/0000-0002-1574-7591)
- Andrea Bulgarelli (ORCID: https://orcid.org/0009-0002-2917-6125)
Institutions
- University of Helsinki (FI)
- University of Bonn (DE)
- Cyprus Institute (CY)
- Deutsches Elektronen-Synchrotron DESY (DE)
- RIKEN Center for Advanced Intelligence Project (JP)
- Berlin Institute for the Foundations of Learning and Data (DE)
- Technische Universität Berlin (DE)
- University of Turin (IT)
Publication Details
- Journal
- Physical Review Letters
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1103/physrevlett.134.151601
- Citations
- 4
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 5.29
Funders
- Simons Foundation
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Horizon 2020 Framework Programme