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arXiv:2607.27009

Embedded quantum computing for many-body surface reaction

Dedong Wan, Xiaopeng Li, Yi Fan, Jie Liu, Xiongzhi Zeng, Zhenyu Li

quant-phphysics.chem-phSubmitted Jul 29, 2026• Announced Jul 30, 2026• 0 citations
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Abstract

Predictive simulations of catalytic interfaces require correlated electronic-structure treatments that describe localized chemical transformations while retaining the influence of the extended metallic environment. We introduce QC-DFET, a quantum-computing density-functional embedding framework that maps surface-reaction active spaces to compact, environment-aware qubit Hamiltonians. A reaction-consistent active-space protocol preserves orbital continuity along reaction coordinates, while quantum-selected configuration interaction based on measurements from the Zuchongzhi superconducting quantum processor and strongly contracted perturbation theory capture static and dynamic correlation. On Cu(111), QC-DFET treats active spaces up to 28 qubits and is validated through a hierarchy of experimentally constrained surface-chemistry challenges. H2 dissociation/desorption tests balanced bond breaking and recombination barriers, CO adsorption tests site selectivity and metal-adsorbate bonding, and formate hydrogenation tests competing hydrogenation branches with different kinetic and thermodynamic signatures. Across these cases, QC-DFET reproduces bidirectional H2 barriers, recovers the observed top-site preference and adsorption strength of CO, and reconciles the experimentally benchmarked H2COO* reverse barrier with the lower forward barrier to HCOOH*. These results establish embedded quantum computing as a practical route to correlated surface-reaction energetics.

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Predictive simulations of catalytic interfaces require correlated electronic-structure treatments that describe localized chemical transformations while retaining the influence of the extended metallic environment. We introduce QC-DFET, a quantum-computing density-functional embedding framework that maps surface-reaction active spaces to compact, environment-aware qubit Hamiltonians. A reaction-consistent active-space protocol preserves orbital continuity along reaction coordinates, while quantum-selected configuration interaction based on measurements from the Zuchongzhi superconducting quantum processor and strongly contracted perturbation theory capture static and dynamic correlation. On Cu(111), QC-DFET treats active spaces up to 28 qubits and is validated through a hierarchy of experimentally constrained surface-chemistry challenges. H2 dissociation/desorption tests balanced bond breaking and recombination barriers, CO adsorption tests site selectivity and metal-adsorbate bonding, and formate hydrogenation tests competing hydrogenation branches with different kinetic and thermodynamic signatures. Across these cases, QC-DFET reproduces bidirectional H2 barriers, recovers the observed top-site preference and adsorption strength of CO, and reconciles the experimentally benchmarked H2COO* reverse barrier with the lower forward barrier to HCOOH*. These results establish embedded quantum computing as a practical route to correlated surface-reaction energetics.
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Quantum-chemical simulations are essential for predicting energies of chemical reactions. Accurately solving the many-body Schrödinger equation for reagent and product states of most relevant chemical processes is, however, unfeasible. Quantum computing offers a pathway for predicting energies of correlated electronic systems with localized interactions. Here, we apply a quantum embedding approach for investigating oxygen reduction reactions at the electrode surface in Lithium batteries, a representative example of energetic analysis in localized chemical reactions. We employ an active space selection method based on density difference analysis for identifying the orbitals involved in the reaction. Leveraging the Local Unitary Cluster Jastrow ansatz for state preparation, the active-space orbitals are then processed on a quantum computer. As quantum algorithms, we use Sample-based Quantum Diagonalization, SQD, and its extended version, Ext-SQD, which integrates electronic excitations into the quantum-selected electronic configuration subspace. The largest configurations are represented by quantum circuits mapped onto 80 qubits of an IBM Heron R2 quantum processing unit. For up to 12 orbitals, we are able to benchmark the quantum-computed reaction energies against results obtained with Complete Active Space Configuration Interaction. For benchmarking results in active spaces as large as 32 orbitals, we resort to Heat-Bath Configuration Interaction and Coupled Cluster Singles and Doubles calculations, respectively. At 27 orbitals, the Ext-SQD results exhibit prediction accuracy improvements with regard to the standard, quantum-chemical reference methods that remain computationally feasible at that scale. The results indicate the potential of sample-based quantum diagonalization for performing high-accuracy reaction modeling in chemistry and materials science.
Abstract Bridging the gap between realistic surface catalysis models from density functional theory (DFT) and the input requirements of quantum algorithms is a non-trivial task. Surface DFT calculations typically model extended slabs and yield total energies or reaction energetics, whereas quantum algorithms (such as variational quantum eigensolvers) require a many-body Hamiltonian (e.g. a list of fermionic or qubit operators) as input. We present an open-source, reproducible software workflow that takes an optimized DFT slab model of a catalyst surface and produces a few-body Hamiltonian suitable for quantum simulation. Starting from a DFT-optimized slab geometry, the workflow extracts a localized cluster around the active site, identifies an appropriate set of active orbitals, and constructs the corresponding fermionic and qubit Hamiltonians. Orbital selection is guided by manual cross-referencing of semiempirical orbital contributions across neutral and anionic cluster states, using localisation and energetic proximity to the HOMO as selection criteria. This end-to-end pipeline enables researchers to bridge surface science and quantum computing in a transparent and automated manner. We demonstrate the workflow on a representative electrochemical system: a hydrogen-based adsorbate (H* or OH*) on an IrO2_22​ catalyst surface. For this system, our software produces a 14-qubit Hamiltonian (derived from a 7-orbital active space) that can be directly used in quantum algorithms such as the Variational Quantum Eigensolver (VQE \cite{VQE}). This work provides a reproducible, end-to-end pipeline bridging periodic surface DFT models and quantum-ready Hamiltonians for heterogeneous catalytic systems, enabling direct integration of realistic surface chemistry with near-term quantum algorithms.
Abstract Bridging realistic surface-catalysis models from density functional theory (DFT) and the many-body Hamiltonian inputs required by quantum algorithms remains a practical obstacle: surface DFT yields total energies and densities, whereas algorithms such as the variational quantum eigensolver require an explicit fermionic or qubit operator. We present an open-source, reproducible workflow that takes a DFT-optimised slab, extracts a hydrogen-capped cluster around the active site, selects a frontier active space automatically, and constructs the corresponding fermionic and qubit Hamiltonians. Heavy-element cores are treated with an effective core potential (ECP) at the Hartree–Fock stage, an established molecular-cluster strategy for quantum-computing catalysis workflows. WedemonstratethepipelineonH*/OH* adsorption on rutile IrO2(110) at two adsorption sites, producing 14-qubit Hamiltonians from (8e, 7o) active spaces. The pipeline is validated end to end: the Hartree–Fock reference reconstructed from the qubit Hamiltonian reproduces the self-consistent-field energy to better than 10 −6 Hartree, and exact diagonalisation yields physically sensible static correlation energies at the milli-Hartree scale. The entire active-space pipeline runs on commodity hardware without high-performance computing. We additionally document a methodological caution: the def2-SVP basis is defined for Iridium only together with its ECP, so an all-electron treatment in this basis is ill-defined rather than merely approximate.
Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits. This framework was applied to prototypical example metal hydrogenation reactions: the coupling between hydrogen and Li2, Li3, and Li4 clusters. Particular attention was paid to the computation of barriers and reaction energies. The predicted reaction profiles compare well with advanced classical quantum chemistry methods, demonstrating the potential of the quantum embedding algorithm to map out reaction profiles of realistic gas-phase chemical reactions to ascertain qualitative energetic trends. Additionally, the predicted potential energy curves provide a benchmark to compare against both current and future quantum embedding approaches.
Abstract Modeling electronic systems is an important application for quantum computers. In the context of materials science, an important open problem is the computational description of chemical reactions on surfaces. In this work, we outline a workflow to model the adsorption and reaction of molecules on surfaces using quantum computing algorithms. We develop and compare two local embedding methods for the systematic determination of active spaces. These methods are automated and based on the physics of molecule-surface interactions and yield systematically improvable active spaces. Furthermore, to reduce the quantum resources required for the simulation of the selected active spaces using quantum algorithms, we introduce a technique for exact and automated circuit simplification. This technique is applicable to a broad class of quantum circuits and critical to enable demonstration on near-term quantum devices. We apply the proposed combination of active-space selection and circuit simplification to the dissociation of water on a magnesium surface using classical simulators and quantum hardware. Our study identifies reactions of molecules on surfaces, in conjunction with the proposed algorithmic workflow, as a promising research direction in the field of quantum computing applied to materials science.