Feasibility of accelerating homogeneous catalyst discovery with fault-tolerant quantum computers
Nicole Bellonzi (1), Alexander Kunitsa (1), Joshua T. Cantin (2, 3),, Jorge A. Campos-Gonzalez-Angulo (2), Maxwell D. Radin (1), Yanbing Zhou (1),, Peter D. Johnson (1), Luis A. Mart\'inez-Mart\'inez (2, 3), Mohammad Reza, Jangrouei (2, 3), Aritra Sankar Brahmachari (4)

TL;DR
This paper assesses the potential of fault-tolerant quantum computers to significantly speed up the discovery of homogeneous catalysts, focusing on nitrogen fixation, and compares quantum and classical computational costs.
Contribution
It introduces a framework for evaluating quantum algorithms for catalyst discovery and estimates their economic and computational feasibility.
Findings
Quantum algorithms could reduce computational costs compared to classical methods.
Estimated runtime for quantum calculations is around 139,000 QPU-hours.
Quantum computing shows promise for accelerating catalyst discovery in chemical industries.
Abstract
The industrial manufacturing of chemicals consumes a significant amount of energy and raw materials. In principle, the development of new catalysts could greatly improve the efficiency of chemical production. However, the discovery of viable catalysts can be exceedingly challenging because it is difficult to know the efficacy of a candidate without experimentally synthesizing and characterizing it. This study explores the feasibility of using fault-tolerant quantum computers to accelerate the discovery of homogeneous catalysts for nitrogen fixation, an industrially important chemical process. It introduces a set of ground-state energy estimation problems representative of calculations needed for the discovery of homogeneous catalysts and analyzes them on three dimensions: economic utility, classical hardness, and quantum resource requirements. For the highest utility problem considered,…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Stochastic Gradient Optimization Techniques · Machine Learning in Materials Science
