Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation

Abstract

Direct sampling from a Slater determinant is combined with an autoregressive deep neural network as a Jastrow factor into a fully autoregressive Slater-Jastrow ansatz for variational quantum Monte Carlo, which allows for uncorrelated sampling. The elimination of the autocorrelation time leads to a stochastic algorithm with provable cubic scaling (with a potentially large prefactor), i.e. the number of operations for producing an uncorrelated sample and for calculating the local energy scales like $\mathcal{O}(N_s^3)$ with the number of orbitals $N_s$. The implementation is benchmarked on the two-dimensional $t-V$ model of spinless fermions on the square lattice.

60 pages; Submission to SciPost

References in corpus (10)

• WaveNet: A Generative Model for Raw Audio
• Determinantal point processes for machine learning
• QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems part I: spin chains
• Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems
• Weak binding between two aromatic rings: feeling the van der Waals attraction by quantum Monte Carlo methods
• Multi-Determinant Wave-functions in Quantum Monte Carlo
• Efficient Continuous-time Quantum Monte Carlo Method for the Ground State of Correlated Fermions
• Symmetry projected Jastrow mean field wavefunction in variational Monte Carlo
• On Representing (Anti)Symmetric Functions
• $O(N^2)$ Universal Antisymmetry in Fermionic Neural Networks

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• Automatic Order Detection and Restoration Through Systematically Improvable Variational Wave Functions
• Addressing the Infinite Variance Problem in Fermionic Monte Carlo Simulations: Retrospective Error Remediation and the Exact Bridge Link Method