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arXiv:2512.19778 · Quantum Computing, Communication, and Simulation

Validation of quantum computing for transition metal oxide-based automotive catalysis

Yuntao Gu, Louis G. Hector, Paolo Giusto, Matthew Titsworth, Alok Warey, Dnyanesh Gajanan Rajpathak, Eser Atesoglu

Published Mar 5, 2026• 0 citations
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Abstract

Quantum computing presents a promising alternative to classical computational methods for modeling strongly correlated materials with partially filled d orbitals. In this study, we perform a comprehensive quantum resource estimation using quantum phase estimation (QPE) and qubitization techniques for transition metal oxide molecules and a Pd zeolite catalyst fragment. Using the binary oxide molecules TiO, MnO, and FeO, we validate our active space selection and benchmarking methodology, employing classical multireference methods such as complete active space self-consistent field (CASSCF) and N-electron valence state perturbation theory (NEVPT2). We then apply these methods to estimate the quantum resources required for a full-scale quantum simulation of a $Z_2Pd$ ($Z=Al_2Si_{22}O_{48}$) fragment taken from the $Pd/2(Al_xSi_{(1-x)})$ catalyst family where x=Si/Al. Our analysis demonstrates that for large Pd zeolite systems, simulations achieving chemical accuracy would require ~$10^6-10^7$ physical qubits, and range that is consistent with the projected capabilities of future fault-tolerant quantum devices. We further explore the impact of active space size, basis set quality, and phase estimation error on the required qubit and gate counts. These findings provide a roadmap for near-term and future quantum simulations of industrially relevant catalytic materials, offering insights into the feasibility and scaling of quantum chemistry applications in materials science.

DOI
10.1117/12.3083095

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Quantum computing presents a promising alternative to classical computational methods for modeling strongly correlated materials with partially filled d orbitals. In this study, we perform a comprehensive quantum resource estimation using quantum phase estimation (QPE) and qubitization techniques for transition metal oxide molecules and a Pd zeolite catalyst fragment. Using the binary oxide molecules TiO, MnO, and FeO, we validate our active space selection and benchmarking methodology, employing classical multireference methods such as complete active space self-consistent field (CASSCF) and N-electron valence state perturbation theory (NEVPT2). We then apply these methods to estimate the quantum resources required for a full-scale quantum simulation of a Z2PdZ_2PdZ2​Pd (Z=Al2Si22O48Z=Al_2Si_{22}O_{48}Z=Al2​Si22​O48​) fragment taken from the Pd/2(AlxSi(1−x))Pd/2(Al_xSi_{(1-x)})Pd/2(Alx​Si(1−x)​) catalyst family where x=Si/Al. Our analysis demonstrates that for large Pd zeolite systems, simulations achieving chemical accuracy would require ~106−10710^6-10^7106−107 physical qubits, and range that is consistent with the projected capabilities of future fault-tolerant quantum devices. We further explore the impact of active space size, basis set quality, and phase estimation error on the required qubit and gate counts. These findings provide a roadmap for near-term and future quantum simulations of industrially relevant catalytic materials, offering insights into the feasibility and scaling of quantum chemistry applications in materials science.
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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.
Numerous reports claim that quantum advantage, which should emerge as a direct consequence of the advent of quantum computers, will herald a new era of chemical research because it will enable scientists to perform the kinds of quantum chemical simulations that have not been possible before. Such simulations on quantum computers, promising a significantly greater accuracy and speed, are projected to exert a great impact on the way we can probe reality, predict the outcomes of chemical experiments, and even drive design of drugs, catalysts, and materials. In this work we review the current status of quantum hardware and algorithm theory and examine whether such popular claims about quantum advantage are really going to be transformative. We go over subtle complications of quantum chemical research that tend to be overlooked in discussions involving quantum computers. We estimate quantum computer resources that will be required for performing calculations on quantum computers with chemical accuracy for several types of molecules. In particular, we directly compare the resources and timings associated with classical and quantum computers for the molecules H2_22​ for increasing basis set sizes, and Cr2_22​ for a variety of complete active spaces (CAS) within the scope of the CASCI and CASSCF methods. The results obtained for the chromium dimer enable us to estimate the size of the active space at which computations of non-dynamic correlation on a quantum computer should take less time than analogous computations on a classical computer. Using this result, we speculate on the types of chemical applications for which the use of quantum computers would be both beneficial and relevant to industrial applications in the short term.
The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25-100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, such as multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.
The quantum computation of electronic energies can break the curse of dimensionality that plagues manyparticle quantum mechanics.It is for this reason that a universal quantum computer has the potential to fundamentally change computational chemistry and materials science, areas in which strong electron correlations present severe hurdles for traditional electronic structure methods.Here we present a state-of-the-art analysis of accurate energy measurements on a quantum computer for computational catalysis, using improved quantum algorithms with more than an order of magnitude improvement over the best previous algorithms.As a prototypical example of local catalytic chemical reactivity we consider the case of a ruthenium catalyst that can bind, activate, and transform carbon dioxide to the high-value chemical methanol.We aim at accurate resource estimates for the quantum computing steps required for assessing the electronic energy of key intermediates and transition states of its catalytic cycle.In particular, we present quantum algorithms for double-factorized representations of the four-index integrals that can significantly reduce the computational cost over previous algorithms, and we discuss the challenges of increasing active space sizes to accurately deal with dynamical correlations.We address the requirements for future quantum hardware in order to make a universal quantum computer a successful and reliable tool for quantum computing enhanced computational materials science and chemistry, and identify open questions for further research.
Quantum chemistry calculations of large, strongly correlated systems are typically limited by the computation cost that scales exponentially with the size of the system. Quantum algorithms, designed specifically for quantum computers, can alleviate this, but the resources required are still too large for today's quantum devices. Here we present a quantum algorithm that combines a localization of multireference wave functions of chemical systems with quantum phase estimation (QPE) and variational unitary coupled cluster singles and doubles (UCCSD) to compute their ground state energy. Our algorithm, termed "local active space unitary coupled cluster" (LAS-UCC), scales linearly with system size for certain geometries, providing a polynomial reduction in the total number of gates compared with QPE, while providing accuracy above that of the variational quantum eigensolver using the UCCSD ansatz and also above that of the classical local active space self-consistent field. The accuracy of LAS-UCC is demonstrated by dissociating (H2_22​)2_22​ into two H2_22​ molecules and by breaking the two double bonds in trans-butadiene and resources estimates are provided for linear chains of up to 20 H2_22​ molecules.