Quadratic Quantum Variational Monte Carlo

Baiyu Su, qiang liu

Keywords: AI for science, Machine learning for physics, Machine learning for chemistry, Quantum physics, Variational Monte Carlo, MCMC, Transformers

TL;DR: We proposed Q^2VMC, a method to enhance efficiency and accuracy of quantum variational Monte Carlo methods.

Abstract: This paper introduces the Quadratic Quantum Variational Monte Carlo (Q$^2$VMC) algorithm, an innovative algorithm in quantum chemistry that significantly enhances the efficiency and accuracy of solving the Schrödinger equation. Inspired by the discretization of imaginary-time Schrödinger evolution, Q$^2$VMC employs a novel quadratic update mechanism that integrates seamlessly with neural network-based ansatzes. Our extensive experiments showcase Q$^2$VMC's superior performance, achieving faster convergence and lower ground state energies in wavefunction optimization across various molecular systems, without additional computational cost. This study not only advances the field of computational quantum chemistry but also highlights the important role of discretized evolution in variational quantum algorithms, offering a scalable and robust framework for future quantum research.

Primary Area: Machine learning for physical sciences (for example: climate, physics)

Submission Number: 19653