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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.18840 |
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| _version_ | 1866915396924211200 |
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| author | Hou, Pengcheng Wang, Tao Cerkoney, Daniel Cai, Xiansheng Li, Zhiyi Deng, Youjin Wang, Lei Chen, Kun |
| author_facet | Hou, Pengcheng Wang, Tao Cerkoney, Daniel Cai, Xiansheng Li, Zhiyi Deng, Youjin Wang, Lei Chen, Kun |
| contents | Quantum field theory (QFT) for interacting many-electron systems is fundamental to condensed matter physics, yet achieving accurate solutions confronts computational challenges in managing the combinatorial complexity of Feynman diagrams, implementing systematic renormalization, and evaluating high-dimensional integrals. We present a unifying framework that integrates QFT computational workflows with an AI-powered technology stack. A cornerstone of this framework is representing Feynman diagrams as computational graphs, which structures the inherent mathematical complexity and facilitates the application of optimized algorithms developed for machine learning and high-performance computing. Consequently, automatic differentiation, native to these graph representations, delivers efficient, fully automated, high-order field-theoretic renormalization procedures. This graph-centric approach also enables sophisticated numerical integration; our neural-network-enhanced Monte Carlo method, accelerated via massively parallel GPU implementation, efficiently evaluates challenging high-dimensional diagrammatic integrals. Applying this framework to the uniform electron gas, we determine the quasiparticle effective mass to a precision significantly surpassing current state-of-the-art simulations. Our work demonstrates the transformative potential of integrating AI-driven computational advances with QFT, opening systematic pathways for solving complex quantum many-body problems across disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18840 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An AI-powered Technology Stack for Solving Many-Electron Field Theory Hou, Pengcheng Wang, Tao Cerkoney, Daniel Cai, Xiansheng Li, Zhiyi Deng, Youjin Wang, Lei Chen, Kun High Energy Physics - Theory Strongly Correlated Electrons Machine Learning High Energy Physics - Phenomenology Computational Physics Quantum field theory (QFT) for interacting many-electron systems is fundamental to condensed matter physics, yet achieving accurate solutions confronts computational challenges in managing the combinatorial complexity of Feynman diagrams, implementing systematic renormalization, and evaluating high-dimensional integrals. We present a unifying framework that integrates QFT computational workflows with an AI-powered technology stack. A cornerstone of this framework is representing Feynman diagrams as computational graphs, which structures the inherent mathematical complexity and facilitates the application of optimized algorithms developed for machine learning and high-performance computing. Consequently, automatic differentiation, native to these graph representations, delivers efficient, fully automated, high-order field-theoretic renormalization procedures. This graph-centric approach also enables sophisticated numerical integration; our neural-network-enhanced Monte Carlo method, accelerated via massively parallel GPU implementation, efficiently evaluates challenging high-dimensional diagrammatic integrals. Applying this framework to the uniform electron gas, we determine the quasiparticle effective mass to a precision significantly surpassing current state-of-the-art simulations. Our work demonstrates the transformative potential of integrating AI-driven computational advances with QFT, opening systematic pathways for solving complex quantum many-body problems across disciplines. |
| title | An AI-powered Technology Stack for Solving Many-Electron Field Theory |
| topic | High Energy Physics - Theory Strongly Correlated Electrons Machine Learning High Energy Physics - Phenomenology Computational Physics |
| url | https://arxiv.org/abs/2403.18840 |