15 citations · 24 across the 9 of their papers we have counts for
9 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 Soljačić
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 Soljačić
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…
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…
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…
Electron bubbles in highly excited states of the lowest Landau level
David D. Dai, Liang Fu
We study the entire energy spectrum of an electron droplet in the lowest Landau level. By exact diagonalization calculations, we find highly excited states in the middle of the spe…
Simulating moiré quantum matter with neural network
Di Luo, David D. Dai, Liang Fu
Moiré materials provide an ideal platform for exploring quantum phases of matter. However, solving the many-electron problem in moiré systems is challenging due to strong correlati…
Electrically controlled interlayer trion fluid in electron-hole bilayers
Ruishi Qi, Qize Li, Zuocheng Zhang +12
The combination of repulsive and attractive Coulomb interactions in a quantum electron(e)-hole(h) fluid can give rise to novel correlated phases of multiparticle charge complexes s…
Pairing-based graph neural network for simulating quantum materials
Di Luo, David D. Dai, Liang Fu
We develop a pairing-based graph neural network for simulating quantum many-body systems. Our architecture augments a BCS-type geminal wavefunction with a generalized pair amplitud…