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Status: Unchecked ×Keyword: non-relativistic quantum field theory ×Clear all

2 claims from 1 paper

1. Physics and Astronomy › Quantum many-body systems

   Variational Neural-Network Ansatz for Continuum Quantum Field Theory

   Martyn, Najafi and Di Luo · Physical Review Letters · 2023

   The paper introduces neural-network quantum field states, a deep learning ansatz for applying the variational principle to non-relativistic continuum quantum field theories, and uses it to approximate ground states.

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   1. UncheckedThe authors' method uses a Deep Sets neural network to describe, all at once, the wave functions for every possible number of particles in a quantum field state.“Our ansatz uses the Deep Sets neural network architecture to simultaneously parameterize all of the $n$-particle wave functions comprising a quantum field state.”
   2. UncheckedThe authors use a neural-network ansatz to approximate ground states of several field theories, including inhomogeneous and long-range ones.“We employ our ansatz to approximate ground states of various field theories, including an inhomogeneous system and a system with long-range interactions, thus demonstrating a powerful new tool for probing quantum field theories.”