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Trapped Ion Quantum Computing Quantum Machine Learning

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

arXiv

Authors: Andrea Bulgarelli, Elia Cellini, Karl Jansen, Stefan Kühn, Alessandro Nada, Shinichi Nakajima, Kim A. Nicoli, Marco Panero

Year

2024

Paper ID

37952

Status

Preprint

Abstract Read

~2 min

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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.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2410.14466 [2] arXiv https://arxiv.org/abs/2410.14466 [3] Publisher https://arxiv.org/abs/2410.14466

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