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Andrea Bulgarelli¹, Elia Cellini¹, Karl Jansen^2,3

¹University of Turin and INFN, Department of Physics, Turin unit, Via Pietro Giuria 1, I-10125 Turin, Italy.

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Summary

This summary is machine-generated.

We developed a new method using generative models to calculate Rényi entanglement entropies in lattice quantum field theory. This technique improves upon existing Monte Carlo calculations for quantum systems.

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Area of Science:

Quantum Field Theory
Computational Physics
Machine Learning

Background:

Calculating entanglement entropies is crucial for understanding quantum many-body systems.
Traditional numerical methods like Monte Carlo face challenges in efficiency and scaling.
Generative models offer a new paradigm for complex system simulations.

Purpose of the Study:

To introduce a novel numerical technique for computing Rényi entanglement entropies.
To leverage generative models and flow-based approaches for lattice quantum field theory.
To demonstrate the efficacy of the proposed method against established techniques.

Main Methods:

Utilizing generative models, specifically flow-based approaches.
Combining the replica trick with a custom neural network architecture.
Implementing the technique around a lattice defect connecting two replicas.
Testing on the phi^4 scalar field theory in 2D and 3D.

Main Results:

The novel technique outperforms state-of-the-art Monte Carlo calculations.
The method shows promising scaling behavior with respect to defect size.
Successful application to phi^4 scalar field theory in two and three dimensions.

Conclusions:

Generative models provide a powerful new tool for quantum information studies in lattice field theory.
The proposed method offers a more efficient and scalable approach to entanglement entropy calculations.
This work opens avenues for applying machine learning to fundamental problems in quantum physics.