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  1. Superconductivity in the two-dimensional Hubbard model revealed by neural quantum states

    Publication
    Whether the ground state of the square lattice Hubbard model exhibits superconductivity remains a major open question, central to understanding high temperature cuprate superconductors and ultra-cold fermions …

  2. Simulating the Two-Dimensional \(t\text\(-\)J\) Model at Finite Doping with Neural Quantum States

    Publication
    Simulating large, strongly interacting fermionic systems remains a major challenge for existing numerical methods. In this work, we introduce Gutzwiller projected hidden fermion determinant states (G-HFDS) to …

  3. Double descent: When do neural quantum states generalize?

    Publication
    Neural quantum states (NQS) provide flexible wavefunction parameterizations for numerical studies of quantum many-body physics. While inspired by deep learning, it remains unclear to what extent NQS …

  4. Neural Network-Augmented Pfaffian Wave-functions for Scalable Simulations of Interacting Fermions

    Publication
    Developing accurate numerical methods for strongly interacting fermions is crucial for improving our understanding of various quantum many-body phenomena, especially unconventional superconductivity. Recently, neural quantum states have …

  5. Interpretable Scaling Behavior in Sparse Subnetwork Representations of Quantum States

    Publication
    The Lottery Ticket Hypothesis (LTH) posits that within overparametrized neural networks, there exist sparse subnetworks that are capable of matching the performance of the original model when trained …