Author

David D. Dai

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Preprint Sep 2026

Fast, Accurate, and Scalable Fermionic Neural Networks via Translation Equivariance

We demonstrate that designing a neural quantum state to be an exact eigenstate of the Hamiltonian's symmetries significantly improves both training speed and final variational energy. For the 2D electron gas, we design TorFormer, a neural network wavefunction which is an exact eigenstate of the total momentum. TorForme...

David D. Dai, Yen-Ting Lin, Marin Soljačić · 0 citations

Preprint Jul 2026

Learning the closest Slater determinant

Learning compact, interpretable descriptions of quantum many-body states is an important task in quantum science. We study the task of learning the Slater determinant with maximum fidelity to an arbitrary fermionic many-body state, with motivation from both Hartree-Fock methods and agnostic tomography. Given an $n$-fer...

Nisarga Paul, Hai-Meng Zhao, David D. Dai · 0 citations