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- W4310633718 abstract "Physicists dating back to Feynman have lamented the difficulties of applying the variational principle to quantum field theories. In non-relativistic quantum field theories, the challenge is to parameterize and optimize over the infinitely many $n$-particle wave functions comprising the state's Fock space representation. Here we approach this problem by introducing neural-network quantum field states, a deep learning ansatz that enables application of the variational principle to non-relativistic quantum field theories in the continuum. Our ansatz uses the Deep Sets neural network architecture to simultaneously parameterize all of the $n$-particle wave functions comprising a quantum field state. 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." @default.
- W4310633718 created "2022-12-13" @default.
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- W4310633718 date "2022-12-01" @default.
- W4310633718 modified "2023-09-26" @default.
- W4310633718 title "Variational Neural-Network Ansatz for Continuum Quantum Field Theory" @default.
- W4310633718 doi "https://doi.org/10.48550/arxiv.2212.00782" @default.
- W4310633718 hasPublicationYear "2022" @default.
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