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A Reproducible Workflow for Extracting Quantum Hamiltonians from Surface-Adsorbate Models

Manisha Malhotra, F. Rinaldi, Neil Ramarapu

Published May 13, 2026• 0 citations
Publisher

Abstract

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 IrO$_2$ 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.

DOI
10.21203/rs.3.rs-9495573/v1

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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.
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.
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.
Quantum simulation has been proposed as a route to treating strongly correlated electronic structure problems relevant to catalysis, but most existing studies focus on faulttolerant algorithms beyond current hardware capabilities. Here we assess the feasibility of near-term quantum simulation for catalytic materials through industrially relevant use cases. We consider zeolite clusters from classical QM/MM simulations relevant to NH3-SCR, and construct electronic active-space and embedding-based models of the catalytic active site. Using variational quantum eigensolver (VQE) ansätze, we estimate qubit requirements and circuit depths to produce the ground-state for both full and reduced Hamiltonians. Direct simulation of the full system is intractable on near-term devices, but fragmentation and active-space approaches, combined with low-rank Local Unitary Cluster Jastrow ansatz constructions, substantially reduce resource requirements. These results indicate that, while full catalytic models remain out of reach, reduced representations of chemically relevant fragments begin to approach the capacity of near-term quantum hardware. This highlights the role of physically informed model reduction in applying quantum simulation to applied materials problems.
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.