Quantum-enhanced neural networks for quantum many-body simulations

TL;DR

This paper introduces a quantum-neural hybrid framework combining quantum circuits and neural networks to improve the modeling of quantum many-body wavefunctions, demonstrating enhanced accuracy and scalability in simulations.

Contribution

It presents a novel hybrid approach that integrates quantum circuits with neural networks, surpassing traditional neural quantum states in expressivity and performance.

Findings

Hybrid method achieves lower relative energy than standalone NQS

Demonstrates scalability in spin systems and quantum chemistry

Enhances expressivity of quantum many-body wavefunction modeling

Abstract

Neural quantum states (NQS) have gained prominence in variational quantum Monte Carlo methods in approximating ground-state wavefunctions. Despite their success, they face limitations in optimization, scalability, and expressivity in addressing certain problems. In this work, we propose a quantum-neural hybrid framework that combines parameterized quantum circuits with neural networks to model quantum many-body wavefunctions. This approach combines the efficient sampling and optimization capabilities of autoregressive neural networks with the enhanced expressivity provided by quantum circuits. Numerical simulations demonstrate the scalability and accuracy of the hybrid ansatz in spin systems and quantum chemistry problems. Our results reveal that the hybrid method achieves notably lower relative energy compared to standalone NQS. These findings underscore the potential of quantum-neural hybrid methods for tackling challenging problems in quantum many-body simulations.