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

Manisha Malhotra, Neil Ramarapu, F. Rinaldi

Published Jul 10, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.21203/rs.3.rs-9495573/v2

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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.
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.
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.
In the near future, material and drug design may be aided by quantum computer assisted simulations. These have the potential to target chemical systems intractable by the most powerful classical computers. However, the resources offered by contemporary quantum computers are still limited, restricting the simulations to very simple molecules. In order to rapidly scale up to more interesting molecular systems, we propose the embedding of the quantum electronic structure calculation into a classically computed environment obtained at the Hartree-Fock (HF) or density functional theory (DFT) level of theory. This result is achieved by constructing an effective Hamiltonian that incorporates a mean field potential describing the action of the inactive electrons on a selected Active Space (AS). The ground state of the AS Hamiltonian is then determined by means of the variational quantum eigensolver algorithm. We show that with the proposed HF and DFT embedding schemes, we can obtain significant energy corrections to the reference HF and DFT calculations for a number of simple molecules in their strongly correlated limit (the dissociation regime) as well as for systems of the size of the oxirane molecule.
Scientific simulations demand methods combining scalability with predictive accuracy. Density Functional Theory (DFT) on High-Performance Computing (HPC) enables large-scale electronic-structure simulations but is limited by approximations affecting strongly correlated systems and band-gap predictions. Quantum computing offers a pathway to address this, though current Noisy Intermediate-Scale Quantum (NISQ) hardware remains constrained by qubit resources, noise, and execution cost. This work presents a hybrid DFT-Quantum Embedding (QDFT) framework integrating classical HPC-based DFT with a quantum electronic-structure solver. Large systems are partitioned to isolate a chemically relevant active space, treated via the Variational Quantum Eigensolver (VQE), while the remaining degrees of freedom are described by DFT. The framework incorporates active-space selection, embedded Hamiltonian construction, symmetry preservation, operator mapping, self-consistent density updating, and modular classical-quantum coupling. We focus on noiseless quantum simulation to systematically evaluate accuracy, convergence, active-space dependence, computational cost, and HPC scalability without hardware noise. Detailed profiling identifies computational bottlenecks and highlights limitations of CPU-based quantum simulation. A QPU runtime-estimation methodology is additionally developed to assess execution requirements on actual quantum hardware. Results demonstrate quantum embedding's potential to improve selected electronic-structure properties while retaining classical HPC's scalability. Noisy quantum simulation and QPU execution remain key future directions, providing a pathway toward practical, scalable HPC-quantum hybrid simulations as hardware matures.
The seamless integration of Density Functional Theory (DFT) with quantum variational algorithms is essential for the predictive simulation of strongly correlated materials. In this work, we present an end-to-end computational pipeline - comprising DFT geometry relaxation, non-self-consistent field (NSCF) calculations, and Wannier-based orbital localization - to prepare active-space Hamiltonians for quantum embedding. We utilize the Adaptive Variational Quantum Eigensolver (ADAPT-VQE) framework, significantly enhanced by a Greedy-Operator Commutativity Partitioning (GOCP) approach and a Taylor-expanded O(5) operator evolution strategy to efficiently manage the exponential scaling of the Hilbert space. We demonstrate this framework through a systematic benchmark study of Li-hBN, mapping the system onto qubit registers and investigating the convergence behavior as the active space is expanded from 8 to 14 spatial orbitals. Our results quantify the relationship between active-space size and computational demand, identifying a critical "scaling wall" where classical simulation costs transition from manageable to intractable. This study provides a rigorous performance baseline for the DFT-to-ADAPT-VQE workflow and offers empirical insights into the memory and processing limits currently facing hybrid quantum-classical architectures using advanced co-processing strategies.