  Recent estimates indicate that the U.S. Department of Defense spends over
\$20 billion USD annually on corrosion-related maintenance. This expenditure is
accompanied by a substantial loss in asset readiness, ranging from 10% to 30%.
Moreover, the global costs associated with corrosion damage have been estimated
at an astonishing \$2.5 trillion USD per year, or approximately 3.4% of global
GDP in 2016. This project aims to describe how quantum computers might be
leveraged to fundamentally change the way material-environment interactions are
modeled for material discovery, selection, and design. This project also seeks
to understand the plausibility and utility of replacing portions of classical
computing workflows with algorithms optimized for quantum computing hardware.
The utility of quantum computers is explored through the lens of two
industrially relevant problems: (1) characterizing magnesium alloy corrosion
properties in aqueous environments and (2) identifying stable niobium-rich
alloys with corrosion resistance at temperatures above 1500K. This paper
presents an end-to-end analysis of the complexity of both classical and quantum
algorithms used in application workflows. Resource estimates are produced using
a custom software package, pyLIQTR, based on the qubitized Quantum Phase
Estimation (QPE) algorithm. Estimates for the two aforementioned applications
show that industrially-relevant computational models that have the potential to
deliver commercial utility require quantum computers with thousands to hundreds
of thousands of logical qubits and the ability to execute $10^{13}$ to
$10^{19}$ T-gates. These estimates represent an upper bound and motivate
continued research into improved quantum algorithms and resource reduction
techniques.
