Hybrid Quantum-Classical Simulations of Graphene Analogues: Adsorption Energetics Beyond DFT

TL;DR

This paper introduces a hybrid quantum-classical framework combining MCSCF and VQE to accurately simulate strongly correlated systems like graphene analogues, surpassing DFT limitations and enabling practical quantum advantage in materials science.

Contribution

The paper develops and benchmarks a hybrid quantum-classical method that accurately predicts binding energies in complex, strongly correlated systems beyond traditional DFT capabilities.

Findings

Framework produces binding energies consistent with high-accuracy quantum methods.

Accurately predicts charge transfer and multireference effects in metal-graphene interactions.

Achieves chemically accurate results for larger systems in the NISQ era.

Abstract

Understanding strongly correlated systems is essential for advancing quantum chemistry and materials science, yet conventional methods like Density Functional Theory (DFT) often fail to capture their complex electronic behavior. To address these limitations, we develop a hybrid quantum-classical framework that integrates Multiconfigurational Self Consistent Field (MCSCF) with the Variational Quantum Eigensolver (VQE). Our initial benchmarks on water dissociation enabled the systematic optimization of key computational parameters, including ansatz selection, active space construction, and error mitigation. Building on this, we extend our approach to investigate the interactions between graphene analogues and water, demonstrating that our framework produces binding energies consistent with high accuracy quantum methods. Furthermore, we apply this methodology to predict the binding energies of transition metals (Fe, Co, Ni) on both pristine and defective graphene analogues, revealing strong charge transfer effects and pronounced multireference character phenomena often misrepresented by standard DFT. In contrast to many existing quantum algorithms that are constrained to small molecular systems, our framework achieves chemically accurate predictions for larger, strongly correlated systems such as metal graphene complexes. This advancement highlights the capacity of hybrid quantum-classical approaches to address complex electronic interactions and demonstrates a practical route toward realizing quantum advantage for real world materials applications in the Noisy Intermediate Scale Quantum (NISQ) era.