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arXiv:2310.10478 · Physical Chemistry Chemical Physics

Quantum hardware calculations of the activation and dissociation of nitrogen on iron clusters and surfaces

Georgia Christopoulou, Cono Di Paola, Floris Elzinga, A. Jallat, David Muñoz Ramo, Michał Krompiec

Published Jan 1, 2024• 2 citations
arXivHTMLPDF

Abstract

methods which account for static and dynamic electron correlation, while arbitrarily accurate for small systems, are generally too expensive to be applied to modelling of catalytic cycles, due to prohibitive time and space computational complexity with respect to the size of the active space. Recent advances in quantum computing give hope for enabling access to accurate ab initio methods at scale. Herein, we present a prototype hybrid quantum-classical workflow for modeling chemical reactions on surfaces, applied to proof-of-concept models of activation and dissociation of nitrogen on small Fe clusters and a single-layer (221) iron surface. First, we determined the structures of species present in the catalytic cycle at DFT level and studied their electronic structure using CASSCF. We show that it is possible to decouple the half-filled Fe-3d band from the Fe-N and N-N bond orbitals, thereby reducing the active space significantly. Subsequently, we translated the CASSCF wavefunctions into corresponding qubit quantum states, using the Adaptive Variational Quantum Eigensolver, and estimated their energies using a state vector simulator, H1-1E quantum emulator and (for selected systems) H1-1 quantum computer. We demonstrated that if a sufficiently small active orbital space is chosen, ground state energies obtained with classical methods and with the quantum computer are in reasonable agreement. We argue that once quantum computing methods are scaled up so that larger active spaces are accessible, they can offer a tremendous practical advantage to the computational catalysis community.

DOI
10.1039/d3cp05167f

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methods which account for static and dynamic electron correlation, while arbitrarily accurate for small systems, are generally too expensive to be applied to modelling of catalytic cycles, due to prohibitive time and space computational complexity with respect to the size of the active space. Recent advances in quantum computing give hope for enabling access to accurate ab initio methods at scale. Herein, we present a prototype hybrid quantum-classical workflow for modeling chemical reactions on surfaces, applied to proof-of-concept models of activation and dissociation of nitrogen on small Fe clusters and a single-layer (221) iron surface. First, we determined the structures of species present in the catalytic cycle at DFT level and studied their electronic structure using CASSCF. We show that it is possible to decouple the half-filled Fe-3d band from the Fe-N and N-N bond orbitals, thereby reducing the active space significantly. Subsequently, we translated the CASSCF wavefunctions into corresponding qubit quantum states, using the Adaptive Variational Quantum Eigensolver, and estimated their energies using a state vector simulator, H1-1E quantum emulator and (for selected systems) H1-1 quantum computer. We demonstrated that if a sufficiently small active orbital space is chosen, ground state energies obtained with classical methods and with the quantum computer are in reasonable agreement. We argue that once quantum computing methods are scaled up so that larger active spaces are accessible, they can offer a tremendous practical advantage to the computational catalysis community.
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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.
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