Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle

TL;DR

This paper introduces a scalable neural quantum state method combined with the kernel polynomial approach to accurately compute optical absorption spectra of large molecules, significantly improving efficiency over traditional FCI calculations.

Contribution

It presents a novel neural-network-based variational quantum Monte Carlo method integrated with the kernel polynomial technique for large-scale optical property calculations.

Findings

Achieves FCI-level accuracy for large molecules with over 50 electrons.

Demonstrates superior scalability and reduced runtime compared to FCI.

Successfully computes optical absorption spectra using neural quantum states.

Abstract

Variational optimization of neural-network quantum state representations has achieved FCI-level accuracy for ground state calculations, yet computing optical properties involving excited states remains challenging. In this work, we present a neural-network-based variational quantum Monte Carlo approach for ab-initio absorption spectra. We leverage parallel batch autoregressive sampling and GPU-supported local energy parallelism to efficiently compute ground states of complex systems. By integrating neural quantum ground states with the kernel polynomial method, our approach accurately calculates absorption spectra for large molecules with over 50 electrons, achieving FCI-level precision. The proposed algorithm demonstrates superior scalability and reduced runtime compared to FCI, marking a significant step forward in optical property calculations for large-scale quantum systems.