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  1. Scalable neural quantum states architecture for quantum chemistry

    Publication

  2. Numerical and geometrical aspects of flow-based variational quantum Monte Carlo

    Publication

  3. Gauge equivariant neural networks for quantum lattice gauge theories

    Publication
    Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials …

  4. Continuous-variable neural-network quantum states and the quantum rotor model

    Publication
    We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization. A simple family of continuous-variable trial wavefunctons …

  5. Overcoming barriers to scalability in variational quantum Monte Carlo

    Publication
    The variational quantum Monte Carlo (VQMC) method received significant attention in the recent past because of its ability to overcome the curse of dimensionality inherent in many-body …

  6. Fermionic Wave Functions from Neural-Network Constrained Hidden States

    Publication
    We introduce a systematically improvable family of variational wave functions for the simulation of strongly correlated fermionic systems. This family consists of Slater determinants in an augmented Hilbert …

  7. Meta Variational Monte Carlo

    Publication
    An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble. A model-agnostic …

  8. Natural evolution strategies and variational Monte Carlo

    Publication
    A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization …

  9. Probabilistic Modeling with Matrix Product States

    Publication
    Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for …

  10. Quantum Natural Gradient

    Publication

  11. Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional Networks

    Publication
    Current mechanisms for knowledge transfer in deep networks tend to either share the lower layers between tasks, or build upon representations trained on other tasks. However, existing work …