Attention-Based Foundation Model for Quantum States
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911316551139328 |
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| author | Zaklama, Timothy Guerci, Daniele Fu, Liang |
| author_facet | Zaklama, Timothy Guerci, Daniele Fu, Liang |
| contents | We present an attention-based foundation model architecture for learning and predicting quantum states across Hamiltonian parameters, system sizes, and physical systems. Using only basis configurations and physical parameters as inputs, our trained neural network is able to produce highly accurate ground state wavefunctions. For example, we build the phase diagram for the 2D square-lattice $t-V$ model with $N$ particles, from only 18 parameters $(V/t,N)$. Thus, our architecture provides a basis for building a universal foundation model for quantum matter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attention-Based Foundation Model for Quantum States Zaklama, Timothy Guerci, Daniele Fu, Liang Strongly Correlated Electrons We present an attention-based foundation model architecture for learning and predicting quantum states across Hamiltonian parameters, system sizes, and physical systems. Using only basis configurations and physical parameters as inputs, our trained neural network is able to produce highly accurate ground state wavefunctions. For example, we build the phase diagram for the 2D square-lattice $t-V$ model with $N$ particles, from only 18 parameters $(V/t,N)$. Thus, our architecture provides a basis for building a universal foundation model for quantum matter. |
| title | Attention-Based Foundation Model for Quantum States |
| topic | Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2512.11962 |