[Paper Review] Quantum computing for corrosion-resistant materials and anti-corrosive coatings design

Nam P. Nguyen, Thomas W. Watts+15 more|arXiv (Cornell University)|Jun 26, 2024

Electron and X-Ray Spectroscopy Techniques4 citations

TL;DR

This paper proposes leveraging quantum computing to accelerate the design of corrosion-resistant materials and anti-corrosive coatings by simulating complex quantum mechanical interactions in magnesium alloys and niobium-rich refractory alloys. Using the qubitized Quantum Phase Estimation (QPE) algorithm via the pyLIQTR software, it estimates that industrially relevant simulations require 10^13 to 10^19 T-gates and thousands to hundreds of thousands of logical qubits, highlighting the current hardware gap and the need for improved quantum algorithms.

ABSTRACT

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 $1962) imes 10^{13}$ T-gates are required for simulating the ground-state energy of these systems under the first quantization encoding using plane waves basis.

Motivation & Objective

• To assess the feasibility of using quantum computing to model complex corrosion processes in engineering materials.
• To estimate the quantum resources required for simulating corrosion-resistant materials, including Mg-rich sacrificial coatings and Nb-rich refractory alloys.
• To compare classical and quantum computational workloads for materials modeling, identifying where quantum advantage may emerge.
• To evaluate the scalability and resource demands of quantum algorithms for industrially relevant systems under realistic energy cutoffs.
• To guide future quantum algorithm development by identifying bottlenecks in qubit and gate count requirements.

Proposed method

• The study employs the qubitized Quantum Phase Estimation (QPE) algorithm as the core quantum computational method for simulating electronic structure in corrosion-relevant materials.
• Resource estimates are generated using the custom software package pyLIQTR, which models logical qubit and T-gate requirements for various system sizes and energy cutoffs.
• The analysis includes explicit simulations and extrapolation using Equation (45) for systems where full computation is infeasible at high energy cutoffs.
• Two material classes are studied: Mg-based systems (monolayer, cluster, and secondary-phase structures) and Nb-rich refractory alloys with interstitial oxygen.
• Simulations are performed across increasing numbers of basis functions, directly tied to energy cutoff (E_cut), to assess scaling behavior.
• Extrapolation reliability is validated using the Dimer model, comparing extrapolated and actual T-gate counts at high E_cut values.

Figure 1 : (Left) 1000 Water Model of Mg Grain Boundary with Mg-Al secondary phase. The golden spheres represent Mg atoms, red oxygen, white hydrogen, and light blue aluminum. (Right) Visualization of a simulation cell of a Nb alloy, Nb 97 Zr 3 Ta 22 W 6 O, with interstitial oxygen. These two geomet

Experimental results

Research questions

• RQ1 What quantum resource requirements (qubits and T-gates) are needed to simulate corrosion processes in Mg-rich sacrificial coatings with high accuracy?
• RQ2 How do resource estimates scale with system size and energy cutoff for Mg-based and Nb-based alloy models?
• RQ3 Can quantum algorithms like QPE provide a practical advantage over classical methods for predicting corrosion resistance in high-temperature refractory alloys?
• RQ4 To what extent can resource estimates be reliably extrapolated from low-energy cutoff simulations to high-accuracy regimes?
• RQ5 What are the upper bounds on logical qubit and T-gate counts required for industrially relevant corrosion modeling using current quantum algorithms?

Key findings

• Simulating Mg-rich sacrificial coatings at high accuracy (E_cut = 30–40 Ry) requires up to 10^19 T-gates and 10^5 logical qubits, based on extrapolated estimates.
• For Nb-rich refractory alloys, explicit resource estimates show T-gate counts in the range of 10^13 to 10^15, requiring 10^4 to 10^5 logical qubits.
• Extrapolation of resource estimates for Mg-based systems using Equation (45) was validated against actual pyLIQTR results, confirming reliability at high E_cut values.
• The monolayer Mg model (257 Mg atoms) requires approximately 10^14 T-gates at E_cut = 30 Ry, while the supercell model exceeds 10^18 T-gates at the same threshold.
• The quadruple-unit Mg17Al12 structure demands over 10^17 T-gates and more than 10^5 logical qubits at E_cut = 40 Ry, indicating extreme resource demands.
• The study establishes that current quantum hardware is insufficient for industrial-scale corrosion modeling, with a clear need for algorithmic improvements and error mitigation.

Figure 2 : Quantum resource estimate summary for selected computational models defined in A and B of the Supplementary Information.