arXiv subjects

David D. Dai

Publications and source records attributed to David D. Dai.

12 recordsLinked to original sources

arXiv2026

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. TorFormer describes both the Fermi liquid and Wigner crystal with no supervision and significantly outperforms Psiformer-based references up to large system sizes. For $r_s = 30.0$ and $40.0$ at $N=91$, we compare TorFormer trained for $8\mathrm{K}$ steps against the previous best NQS, which required $100\mathrm{K}$ training steps. Our improvement to the total energy at $r_s = 40.0$, excluding the trivial Madelung part, is $0.12\%$---enormous compared to the tiny differences separating phases. Relative to Slater-Jastrow-backflow diffusion Monte Carlo, TorFormer's energy decrease is roughly $9.8$ times that of the previous best NQS. Our work demonstrates that neural quantum states can both accurately and efficiently solve large-scale problems.

cond-mat.str-el↗

arXiv2026

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 $n$-fermion wavefunction built from $m$ fermionic modes, we provide classical and quantum algorithms returning a Slater determinant with fidelity within $\varepsilon$ of maximal in time $m^{\text{poly}(n,1/\varepsilon)}$. We prove matching hardness lower bounds, assuming standard complexity conjectures, along some parameter axes. Given access to quantum copies, we prove this can be accomplished with $\text{poly}(m,n,1/\varepsilon)$ copies of $\rho$. We also show that above a fidelity of $2/3$ any stationary point is the unique global maximum while below $2/3$ the optimization landscape can have spurious stationary points, and hence $2/3$ marks a transition point in the optimization landscape for this problem. We apply the algorithm to the Fermi-Hubbard model, extracting the closest Slater determinant from neural quantum state solutions. Together, our results provide algorithmic tools with provable guarantees in understanding fermionic many-body systems with classical or quantum simulation.

quant-ph↗

arXiv2026

Continuum Neural Momentum Eigenstate for Variationally Solving Quasiparticles

We design the first neural quantum state for continuum particles that, for any chosen allowed momentum $\mathbf{k}$, is by construction an exact eigenstate of total momentum with eigenvalue $\mathbf{k}$. Our architecture, EVE, enables off-the-shelf VMC to solve for momentum-sector ground states. We test EVE on 2D bosons with mutual $1/r$ interactions, finding that a single unified ansatz is capable of describing four qualitatively different states: superfluid, roton, crystal, and phonon. At different densities, we extract the underlying phase of matter from the dispersion's shape. At $r_s = 20.0$, we see the roton minimum at finite $k$ expected of a superfluid. At $r_s = 100.0$, we see striking zone folding indicative of crystalline order, with periodically spaced minima representing floating crystals connected by phonon arcs in between. Using density-density correlation functions, we confirm the phase diagnoses and probe the excitations' correlation structures. Finally, we analyze the roton's phase texture and find unexpected multi-particle phase strings, formed when several vortex dipoles merge, leaving two vortices connected by a phase slip.

cond-mat.quant-gas↗

arXiv2026

Essentially No Energy Barrier Between Independent Fermionic Neural Quantum State Minima

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 demonstrate that the NQS loss landscape is more benign and similar to conventional deep learning than previously thought, exhibiting mode connectivity: independently trained NQS are connected by paths in parameter space with essentially no energy barrier. To construct these paths, we develop GeoNEB, a path optimizer integrating efficient stochastic reconfiguration with the nudged elastic band method for constructing minimum energy paths. For the strongly interacting six-electron quantum dot modeled by a $1.6$M-parameter Psiformer, we find two independent minima with expected energy barrier $\sim10^{-5}$ times smaller than the system's overall energy scale and $\sim10^{-3}$ times smaller than the linear path's barrier. The path respects physical symmetry in addition to achieving low energy, with the angular momentum remaining well quantized throughout. Our work is the first to construct optimized paths between independently trained NQS, and it suggests that the NQS loss landscape may not be as pathological as once feared.

cond-mat.dis-nn↗

arXiv2025

Solving and visualizing fractional quantum Hall wavefunctions with neural network

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 for fractional quantum Hall (FQH) liquids, Wigner crystals and other unconventional electron states. Working directly with the full Hilbert space of $N$ electrons confined to a disk, our FNN consistently attains energies lower than LL-projected exact diagonalization (ED) and learns the ground state wavefunction to high accuracy. In low LL mixing regime, our FNN reveals microscopic features in the short-distance behavior of FQH wavefunction beyond the Laughlin ansatz. For moderate and strong LL mixing parameters, the FNN outperforms ED significantly. Moreover, a phase transition from FQH liquid to a crystal state is found at strong LL mixing. Our study demonstrates unprecedented power and universality of FNN based variational method for solving strong-coupling many-body problems with topological order and electron fractionalization.

cond-mat.str-el↗

arXiv2024

Exact Valence-Bond Solid Scars in the Square-Lattice Heisenberg Model

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-even systems and ladders of width 2. Ladders have additional scars corresponding to injecting one or two magnons on top of a parent valence-bond solid scar. These scars have a remarkably simple physical origin based only the angular momentum algebra and cancellations from spin-antialignment within a valence bond. Our comprehensive exact diagonalization calculations suggest that our valence-bond solids exhaust all exact eigenstates in the Heisenberg model except for few-magnon states near the top of the spectrum. Our scars are interesting because they are not part of a tower, have area-law entanglement, break translation symmetry, and exist for Heisenberg models of all spin.

cond-mat.str-el↗

arXiv2024

Electron bubbles in highly excited states of the lowest Landau level

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 spectrum that display unexpected density distribution and pair correlation. We show that these exceptional excited states contain tightly bound electron bubbles with local filling $ν= 1$ that form various ordered structures. Remarkably, these bubble excited states are shown to exist for both the $1/r$ Coulomb interaction and the $1/r^3$ dipole interaction. The experimental realization of bubble excited states in moiré materials under a magnetic field is also discussed.

cond-mat.mes-hall↗

arXiv2024

Simulating moiré quantum matter with neural network

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 correlation effects. We introduce a powerful variational representation of quantum states, many-body neural Bloch wavefunction, to solve many-electron problems in moiré materials accurately and efficiently. Applying our method to the semiconductor heterobilayer WSe2/WS2 , we obtain a generalized Wigner crystal at filling factor n = 1/3, a Mott insulator n = 1, and a correlated insulator with local magnetic moments and antiferromagnetic spin correlation at n = 2. Our neural network approach improves the simulation accuracy of strongly interacting moiré materials and paves the way for discovery of new quantum phases with variational learning principle in a unified framework.

cond-mat.str-el↗

arXiv2024

Strong-coupling phases of trions and excitons in electron-hole bilayers at commensurate densities

We introduce density imbalanced electron-hole bilayers at a commensurate 2 : 1 density ratio as a platform for realizing novel phases involving electrons, excitons and trions. Three length scales are identified which characterize the interplay between kinetic energy, intralayer repulsion, and interlayer attraction. By a combination of theoretical analysis and numerical calculation, we find a variety of strong-coupling phases in different parameter regions, including quantum crystals of electrons, excitons, and trions. We also propose an "excitonic supersolid" phase that features electron crystallization and exciton superfluidity simultaneously. The material realization and experimental signature of these phases are discussed in the context of semiconductor transition metal dichalcogenide bilayers.

cond-mat.str-el↗

arXiv2023

Electrically controlled interlayer trion fluid in electron-hole bilayers

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 such as excitons, trions and biexcitons. Here we report the first experimental realization of an electrically controlled interlayer trion fluid in two-dimensional van der Waals heterostructures. We demonstrate that in the strong coupling regime of electron-hole bilayers, electrons and holes in separate layers can spontaneously form three-particle trion bound states that resemble positronium ions in high energy physics. The interlayer trions can assume 1e-2h and 2e-1h configurations, where electrons and holes are confined in different transition metal dichalcogenide layers. We show that the two correlated holes in 1e-2h trions form a spin-singlet state with a spin gap of ~1meV. By electrostatic gating, the equilibrium state of our system can be continuously tuned into an exciton fluid, a trion fluid, an exciton-trion mixture, a trion-charge mixture or an electron-hole plasma. Upon optical excitation, the system can host novel high-order multiparticle charge complexes including interlayer four-particle complex (tetrons) and five-particle complex (pentons). Our work demonstrates a unique platform to study novel correlated phases of tunable Bose-Fermi mixtures and opens up new opportunities to realize artificial ions/molecules in electronic devices.

cond-mat.mes-hall↗

arXiv2023

Pairing-based graph neural network for simulating quantum materials

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 amplitude parameterized by a graph neural network. Variational Monte Carlo with our neural network simultaneously provides an accurate, flexible, and scalable method for simulating many-electron systems. We apply this method to two-dimensional semiconductor electron-hole bilayers and obtain accurate results on a variety of interaction-induced phases, including the exciton Bose-Einstein condensate, electron-hole superconductor, and bilayer Wigner crystal. Our study demonstrates the potential of physically-motivated neural network wavefunctions for quantum materials simulations.

cond-mat.str-el↗

arXiv2021

Tunable quantum logic gate on photonic qubits with a ladder emitter

We present a scheme to implement a passive and deterministic controlled-variable phase gate on photonic qubits encoded in the frequency basis. Our gate employs a cascade system with the ground to first excited state interacting with the control photon of a given polarization, and the first to second excited state transition interacting with the target photon of the orthogonal polarization. By controlling the relative detuning between the target photon and the frequency of the transition between the first and second excited states of the cascade emitter, we enable any controlled-phase operation from 0 to $π$. This gate does not utilize any active control and needs only a single cascade emitter, enabling low-footprint and more efficient decomposition of quantum circuits, especially those rooted in the quantum Fourier transform.

quant-ph↗