Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges

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Authors: Seongmin Kim, In-Saeng Suh, Travis S. Humble, Thomas L. Beck, eungkyu lee, Tengfei Luo
Cited by: 4
References: 79

Abstract

Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This Perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.

Fields of Study & Subjects

Quantum Computing Algorithms and Architecture Machine Learning in Materials Science Quantum-Dot Cellular Automata

Publication details

Journal / source
ACS Energy Letters
Publisher
American Chemical Society (ACS)
Volume
11
Issue
2
Pages
1182–1192
Publication date
15 January 2026
Language
EN