Harnessing quantum mechanics to solve one of industry's most persistent and costly challenges
Imagine a world where we could predict exactly when and where a bridge, an airplane, or a pipeline would succumb to rust—not through periodic inspections, but through precise computational simulations that account for every atom involved in the process. This vision is moving closer to reality thanks to an unexpected ally: quantum computing.
Corrosion is not merely surface degradation; it is a complex electrochemical battle at the atomic scale that costs the global economy an estimated $2.5 trillion annually 1 . The U.S. Department of Defense alone spends over $20 billion each year on corrosion-related maintenance, suffering asset readiness losses of 10-30% in the process 1 9 .
For decades, scientists have struggled to accurately model these processes using classical computers. The challenge is fundamental: corrosion involves quantum mechanical interactions between atoms, electrons, and their environment—phenomena that classical computers approximate with difficulty. Now, quantum computers, which naturally simulate quantum behavior, are emerging as a powerful tool to bridge research and industry, offering hope for revolutionary advances in materials science and corrosion prevention 1 9 .
The financial impact of corrosion extends far beyond military budgets, affecting virtually every industrial sector.
$2.5T
Annually (3.4% of global GDP)
$20B+
Annual corrosion-related maintenance
| Category | Estimated Cost | Primary Affected Sectors |
|---|---|---|
| Global Economic Impact | $2.5 trillion annually (3.4% of global GDP) | All industrial sectors |
| U.S. Department of Defense | $20+ billion annually | Military equipment, aircraft, ships |
| Asset Readiness Loss | 10-30% reduction | Defense, transportation, energy |
| Maintenance & Replacement | Significant portion of infrastructure budgets | Bridges, pipelines, industrial facilities |
The pursuit of better corrosion-resistant materials is not merely about cost savings—it's about safety, reliability, and sustainability. Developing materials that last longer reduces waste, conserves resources, and prevents catastrophic failures 1 .
To understand why quantum computing offers such promise, we must first appreciate why classical computers find corrosion modeling so challenging.
Corrosion occurs across multiple scales simultaneously:
Electron transfers and chemical bond formation/breaking
Initiation of pits and cracks
Arrangement of atoms and formation of corrosion products
Visible degradation of structures and components
Classical computers typically use density functional theory (DFT) to simulate these quantum-scale interactions, but this method has significant limitations for corrosion processes. Many corrosion mechanisms involve multi-configurational electronic structures—systems where electrons exist in multiple possible states simultaneously. DFT struggles to accurately describe these complex quantum states, leading to approximations that reduce predictive accuracy 9 .
Many existing corrosion models rely heavily on empirical data—information gathered from past observations and experiments. While useful, this approach provides limited predictive power for new materials or extreme environments. As noted in recent research, "classical approaches to modeling corrosion can be limited because they may not accurately capture the complex interactions at the atomic level" 1 .
Unlike classical bits that can only be 0 or 1, quantum bits (qubits) can exist in superposition—representing both 0 and 1 simultaneously. This fundamental property allows quantum computers to naturally simulate quantum mechanical systems, including the electron interactions that drive corrosion processes 1 .
When multiple qubits become entangled—a unique quantum connection where each qubit's state depends on the others—the computational power grows exponentially. A system of just 50 entangled qubits could represent over a quadrillion possible states simultaneously, far beyond the capability of any classical computer 2 .
Either 0 OR 1
0 AND 1 simultaneously
Model electrochemical reactions atom-by-atom with unprecedented accuracy
Forecast how new compositions will behave in corrosive environments
Simulate formation of protective layers and identify failure points
Screen thousands of potential solutions simultaneously
"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" 9 .
In a groundbreaking 2024 study published on arXiv, researchers developed and demonstrated a hybrid quantum-classical workflow specifically tailored for atomistic simulations of corrosion processes 9 .
The research team focused on a critical trigger for the corrosion of aluminum alloys widely used in modern aircraft: the initial step of the oxygen reduction reaction. This reaction is particularly important because it drives the electrochemical process that leads to metal degradation.
The team employed both noisy intermediate-scale quantum (NISQ) approaches for current-generation quantum processors and fault-tolerant quantum algorithms for future, more stable quantum computers. This dual approach allowed them to establish a foundation for both near-term and long-term applications of quantum computing in corrosion science 9 .
Oxygen reduction reaction in aluminum alloys
| Step | Description | Tool Used |
|---|---|---|
| System Identification | Find reaction geometries with multi-configurational electronic structures | Classical computational chemistry |
| Quantum Algorithm Selection | Choose appropriate quantum algorithms for the identified systems | Noisy Intermediate-Scale Quantum (NISQ) and fault-tolerant algorithms |
| Resource Estimation | Calculate quantum computer requirements for accurate simulation | pyLIQTR software package |
| Macroscopic Integration | Connect atomistic results to larger-scale models | Finite element methods |
The study yielded crucial insights—both promising and sobering. Researchers successfully identified specific reaction geometries in aluminum alloy corrosion that exhibit the multi-configurational electronic structures ideal for quantum algorithms. However, their resource estimation revealed that significant advancements in quantum hardware and algorithms are still needed for practical applications.
Identified reaction geometries with multi-configurational electronic structures suitable for quantum algorithms
Need for quantum computers with thousands to hundreds of thousands of logical qubits for industrial applications
For industrially relevant computational models with commercial potential, the study estimated a need for quantum computers with thousands to hundreds of thousands of logical qubits and the ability to execute between 10¹³ and 10¹⁹ T-gates (a specific type of quantum operation) 9 . These estimates represent an upper bound and highlight the importance of continued research into improved quantum algorithms and resource reduction techniques.
Breaking new ground in quantum corrosion modeling requires specialized tools and approaches.
| Component | Function | Example Applications |
|---|---|---|
| Hybrid Quantum-Classical Algorithms | Divide computational tasks between quantum and classical processors | Variational Quantum Eigensolver (VQE) for molecular simulations |
| Quantum Resource Estimators | Predict quantum hardware requirements for specific problems | pyLIQTR software for estimating qubit and gate counts |
| Error Correction Codes | Protect quantum information from decoherence and noise | Gottesman-Kitaev-Preskill (GKP) codes for efficient error correction |
| Advanced Quantum Hardware | Provide physical platforms for running quantum algorithms | Trapped ions, superconducting qubits, photonic processors |
| Material Characterization Tools | Validate quantum computations with real-world data | Scanning electron microscopy, X-ray diffraction, FTIR spectroscopy |
Recent advances in error correction are particularly promising. In 2025, quantum scientists at the University of Sydney demonstrated a type of quantum logic gate using an error-correcting code nicknamed the "Rosetta stone" of quantum computing (technically called Gottesman-Kitaev-Preskill or GKP codes) that drastically reduces the number of physical qubits needed for operation .
"Our experiments have shown the first realization of a universal logical gate set for GKP qubits. We did this by precisely controlling the natural vibrations, or harmonic oscillations, of a trapped ion" — Dr. Tingrei Tan, lead researcher .
While quantum computing for corrosion modeling remains in its early stages, progress is accelerating across multiple fronts.
Plans a quantum-centric supercomputer by 2025 with over 4,000 qubits, focusing on improving circuit quality 4 .
Aims for a useful, error-corrected quantum computer by 2029 4 .
Lightweight materials ideal for aerospace applications but prone to corrosion in aqueous environments 1 .
High-strength materials useful for jet engines but needing improved oxidation resistance at temperatures above 1500K 1 .
The global nature of this research effort is expanding, with initiatives like the UK's National Quantum Computing Centre SparQ grants funding projects such as "QuantiCo: Quantum for Corrosion Chemistry"—a collaboration between Capgemini UK, Airbus, and King's College London to develop quantum-powered molecular embedding techniques for corrosion analysis 5 .
The integration of quantum computing into corrosion science represents more than just an incremental improvement—it promises a fundamental shift in how we understand and combat one of humanity's most persistent and expensive materials challenges.
While practical, large-scale applications may still be years away, the foundation is being laid today through hybrid quantum-classical workflows, advanced error correction techniques, and international collaborations between academia and industry. As quantum hardware continues to mature and algorithms become more sophisticated, we move closer to a future where we can design inherently corrosion-resistant materials from the atomic level up.
The implications extend far beyond economic savings. By unlocking deeper understanding of corrosion mechanisms, quantum computing could help extend the life of critical infrastructure, improve the safety of transportation systems, reduce the environmental impact of material replacement, and open new frontiers in materials design. As one research team concluded, 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" 9 .
The bridge between quantum research and industrial application is under construction—and it's being built to last.