Quantum Computing in Corrosion Modeling: Bridging Research and Industry

TL;DR

This paper introduces a hybrid quantum-classical workflow for atomistic corrosion modeling, focusing on the oxygen reduction reaction in aluminum alloys, and evaluates quantum algorithms' potential and resource requirements for this application.

Contribution

It develops a novel hybrid quantum-classical approach for corrosion simulation, integrating quantum algorithms with classical methods and providing quantum resource estimates for practical implementation.

Findings

Quantum algorithms can explore multi-configurational electronic structures in corrosion processes.

Significant hardware improvements are needed for practical quantum corrosion modeling.

The workflow bridges atomistic simulations with macroscopic corrosion models.

Abstract

Corrosion presents a major challenge to the longevity and reliability of products across various industries, particularly in the aerospace sector. Corrosion arises from chemical processes occurring on an atomistic scale, which lead to macroscopic degradation. Addressing this issue requires multi-scale modeling approaches, which rely on microscopic parameters that are challenging to measure experimentally or model with conventional quantum chemistry techniques. In this work, we develop and demonstrate a hybrid quantum-classical workflow tailored for atomistic simulations of corrosion processes, with a specific focus on the initial step of the oxygen reduction reaction -- a critical trigger for the corrosion of aluminum alloys widely used in modern aircraft. Using a combination of classical quantum chemistry methods and quantum computing frameworks, we identify reaction geometries characterized by multi-configurational electronic structures that are ideal for exploring with quantum algorithms. For the first time in this context, we explore both noisy intermediate-scale quantum and fault-tolerant quantum algorithms for these multi-configurational system, integrating them within a workflow designed to bridge atomistic simulations with macroscopic modeling approaches, such as finite element methods. Furthermore, we conduct a detailed quantum resource estimation to assess when and how quantum computers may play a meaningful role in tackling these problems. Our results demonstrate that significant advancements in quantum hardware but also in algorithms and error correction techniques are needed to make quantum computation practically viable for this class of problems. Nevertheless, this work establishes a critical foundation for applying quantum computation to corrosion modeling and highlights its potential to address complex, business-relevant challenges in materials science.