Validation of Quantum Computing for Transition Metal Oxide-based Automotive Catalysis

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.