journal article·2026-02-13Open access

Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges

Seongmin Kim, In-Saeng Suh, Travis S. Humble, Thomas Beck, Eungkyu Lee, Tengfei Luo

ACS Energy Letters· Volume 11· Issue 2

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.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Quantum Computing Algorithms and Architecture · Machine Learning in Materials Science · Quantum-Dot Cellular Automata

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

National Center for Computational Sciences

Oak Ridge National Laboratory

Quantum Science Center

Department of Electronic Engineering

Kyung Hee University

Department of Aerospace and Mechanical Engineering

University of Notre Dame

Funding

Ministry of Science and ICT, South Korea · U.S. Department of Energy

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