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arxiv 2410.14466 v2 pith:LFCFKJHP submitted 2024-10-18 quant-ph cond-mat.stat-mechcs.LGhep-lat

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

Andrea Bulgarelli , Elia Cellini , Karl Jansen , Stefan K\"uhn , Alessandro Nada , Shinichi Nakajima , Kim A. Nicoli , Marco Panero This is my paper
classification quant-ph cond-mat.stat-mechcs.LGhep-lat
keywords defectentanglementfieldflow-basedlatticetechniquetheoryapproaches
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce a novel technique to numerically calculate R\'enyi 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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Determination of thermodynamics from entanglement entropy in the finite-density O(N) model

    hep-th 2026-07 accept novelty 7.0

    The derivative of entanglement entropy with respect to subregion volume equals the thermal entropy density in the large-subregion limit, verified via lattice simulations of the finite-density O(4) model using dual wor...

  2. Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality

    hep-lat 2026-04 unverdicted novelty 7.0

    A hierarchical generative model for critical lattice scalar field theories achieves orders-of-magnitude lower autocorrelation times than HMC while enabling exact multilevel Monte Carlo.

  3. Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory

    hep-lat 2025-10 unverdicted novelty 6.0

    Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.

  4. Machine learning for four-dimensional SU(3) lattice gauge theories

    hep-lat 2026-04 unverdicted novelty 3.0

    Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results to...