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arXiv · 2512.11962

Attention-Based Foundation Model for Quantum States

Timothy Zaklama·Daniele Guerci·Liang Fu
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Abstract

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.

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Timothy Zaklama, Daniele Guerci, Liang Fu. 2025-12-12. Attention-Based Foundation Model for Quantum States. https://arxiv.org/abs/2512.11962

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