Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
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