MIT paper builds attention-based foundation model for quantum states, 18 training points suffice
jwt0625 · x · 2026-10-18
A shared MIT physics paper, Attention-Based Foundation Model for Quantum States, proposes an attention-based architecture that predicts accurate ground-state wavefunctions from Hamiltonian parameters and system configs alone — e.g. it reconstructs the phase diagram of the 2D square-lattice t-V model from just 18 training points in parameter space, aiming at a universal foundation model for quantum matter. The authors argue physics insight into training data selection is key, and the success of foundation models relies on a smooth mapping from parameter space to wavefunction space. The meme image shows Grim Reaper "Attention" knocking on Quantum's door after Language, Vision, Protein Folding and Coding.
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