5 papers
Learning the closest Slater determinant
Nisarga Paul, Haimeng Zhao, David D. Dai
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…
Continuum Neural Momentum Eigenstate for Variationally Solving Quasiparticles
David D. Dai, Marin SoljaÄiÄ
We design the first neural quantum state for continuum particles that, for any chosen allowed momentum , is by construction an exact eigenstate of total momentum with e…
Essentially No Energy Barrier Between Independent Fermionic Neural Quantum State Minima
David D. Dai, Marin SoljaÄiÄ
Neural quantum states (NQS) have proven highly effective in representing quantum many-body wavefunctions, but their loss landscape remains poorly understood and debated. Here, we d…
Solving and visualizing fractional quantum Hall wavefunctions with neural network
Yi Teng, David D. Dai, Liang Fu
We introduce an attention-based fermionic neural network (FNN) to variationally solve the problem of two-dimensional Coulomb electron gas in magnetic fields, a canonical platform f…
Exact Valence-Bond Solid Scars in the Square-Lattice Heisenberg Model
David D. Dai
We show that the spin-s square-lattice Heisenberg model has exact many-body scars. These scars are simple valence-bond solids with exactly zero energy, and they exist in even-by-ev…