Corrosion is a pervasive issue that impacts the structural integrity and performance of materials across various industries, imposing a significant economic impact globally. In fields like aerospace and defense, developing corrosion-resistant materials is critical, but progress is often hindered by the complexities of material-environment interactions. While computational methods have advanced in designing corrosion inhibitors and corrosion-resistant materials, they fall short in understanding the fundamental corrosion mechanisms due to the highly correlated nature of the systems involved. This paper explores the potential of leveraging quantum computing to accelerate the design of corrosion inhibitors and corrosion-resistant materials, with a particular focus on magnesium and niobium alloys. We investigate the quantum computing resources required for high-fidelity electronic ground-state energy estimation (GSEE), which will be used in our hybrid classical-quantum workflow. Representative computational models for magnesium and niobium alloys show that 2292 to 38598 logical qubits and (1.04 to 1962) × 1013 T-gates are required for simulating the ground-state energy of these systems under the first quantization encoding using plane waves basis.

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Nature

Researchers explore using quantum computing to simulate corrosion in magnesium and niobium alloys for aerospace applications. The study estimates quantum resource requirements for ground-state energy calculations, finding that simulating these materials requires 2,292 to 38,598 logical qubits and up to 1.96 × 10^16 T-gates using plane wave basis encoding. The work proposes a hybrid classical-quantum workflow to accelerate the design of corrosion-resistant materials and inhibitors, addressing limitations of classical computational methods in modeling highly correlated electron systems involved in corrosion mechanisms.

Quantum computing for corrosion simulation: workflow and resource analysis