Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
Source abstract
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
References (82)
Show source-provided references
1. IEA. Global Energy Review 2025 Paris. https://www.iea.org/reports/global-energy-review-2025 (accessed 2025–12–27).
2. 10.1002/aenm.20170179710.1002/aenm.201701797 ↗
3. 10.1021/acsenergylett.2c0196910.1021/acsenergylett.2c01969 ↗
4. 10.1016/j.heliyon.2024.e2972810.1016/j.heliyon.2024.e29728 ↗
5. 10.1021/acsenergylett.2c0140010.1021/acsenergylett.2c01400 ↗
6. 10.1021/acs.chemrev.1c0041210.1021/acs.chemrev.1c00412 ↗
7. 10.1021/acsenergylett.2c0109910.1021/acsenergylett.2c01099 ↗
8. 10.1021/acsenergylett.1c0118410.1021/acsenergylett.1c01184 ↗
9. 10.1038/s41524-022-00765-z10.1038/s41524-022-00765-z ↗
10. 10.1038/s41524-020-0280-210.1038/s41524-020-0280-2 ↗
11. 10.1021/accountsmr.1c0024610.1021/accountsmr.1c00246 ↗
12. 10.1038/s41467-023-37587-610.1038/s41467-023-37587-6 ↗
13. 10.1021/acs.jpclett.3c0033010.1021/acs.jpclett.3c00330 ↗
14. 10.1063/5.006048110.1063/5.0060481 ↗
15. 10.1103/PhysRevResearch.2.01331910.1103/PhysRevResearch.2.013319 ↗
16. 10.1038/s41524-024-01505-110.1038/s41524-024-01505-1 ↗
17. 10.1038/s41586-022-04603-610.1038/s41586-022-04603-6 ↗
18. 10.1016/j.rser.2022.11249310.1016/j.rser.2022.112493 ↗
19. 10.1016/j.apenergy.2024.12324410.1016/j.apenergy.2024.123244 ↗
20. 10.1126/sciadv.adq849210.1126/sciadv.adq8492 ↗
21. 10.1016/j.epsr.2023.10953010.1016/j.epsr.2023.109530 ↗
22. 10.1016/j.heliyon.2024.e3433110.1016/j.heliyon.2024.e34331 ↗
23. 10.1088/2058-9565/abae7d10.1088/2058-9565/abae7d ↗
24. 10.1088/1361-6633/ac8c5410.1088/1361-6633/ac8c54 ↗
25. 10.1103/PhysRevX.10.02106710.1103/PhysRevX.10.021067 ↗
26. 10.1021/acs.chemrev.9b0082910.1021/acs.chemrev.9b00829 ↗
27. 10.1126/science.abb282310.1126/science.abb2823 ↗
28. 10.1016/j.future.2024.04.06010.1016/j.future.2024.04.060 ↗
29. 10.48550/arXiv.2407.2021210.48550/arXiv.2407.20212 ↗
30. 10.1038/s41534-019-0173-810.1038/s41534-019-0173-8 ↗
31. 10.1038/nature0335010.1038/nature03350 ↗
32. 10.1039/C5CP00437C10.1039/C5CP00437C ↗
33. 10.1103/RevModPhys.92.01500310.1103/RevModPhys.92.015003 ↗
34. 10.1021/acs.chemrev.8b0080310.1021/acs.chemrev.8b00803 ↗
35. 10.1063/5.015029110.1063/5.0150291 ↗
36. 10.1021/acs.jctc.5c0103810.1021/acs.jctc.5c01038 ↗
37. 10.1021/acsinfocus.7e901210.1021/acsinfocus.7e9012 ↗
38. 10.1038/s41467-019-10988-210.1038/s41467-019-10988-2 ↗
39. Bauman N. P. · Adv. Quantum Chem. · 2023 · 87 · 14110.1016/bs.aiq.2023.03.006 ↗
40. 10.1126/science.ado628510.1126/science.ado6285 ↗
41. 10.1038/s41534-025-01020-110.1038/s41534-025-01020-1 ↗
42. 10.1038/s41534-019-0209-010.1038/s41534-019-0209-0 ↗
43. 10.1002/qute.20190005210.1002/qute.201900052 ↗
44. 10.1146/annurev-conmatphys-031119-05060510.1146/annurev-conmatphys-031119-050605 ↗
45. 10.1146/annurev-conmatphys-032822-04561910.1146/annurev-conmatphys-032822-045619 ↗
46. 10.1063/1.508816410.1063/1.5088164 ↗
47. 10.1063/1.511581410.1063/1.5115814 ↗
48. 10.48550/arXiv.2508.1176510.48550/arXiv.2508.11765 ↗
49. 10.22331/q-2020-09-21-32710.22331/q-2020-09-21-327 ↗
50. 10.1088/2633-4356/ade35910.1088/2633-4356/ade359 ↗
51. 10.1038/s41534-022-00570-y10.1038/s41534-022-00570-y ↗
52. 10.1126/sciadv.adm676110.1126/sciadv.adm6761 ↗
53. 10.48550/arXiv.1411.402810.48550/arXiv.1411.4028 ↗
54. 10.1038/s41467-023-43479-610.1038/s41467-023-43479-6 ↗
55. 10.1103/PhysRevResearch.4.03302910.1103/PhysRevResearch.4.033029 ↗
56. 10.48550/arXiv.2504.1635010.48550/arXiv.2504.16350 ↗
57. Alam, M.; Ash-Saki, A.; Ghosh, S. Accelerating Quantum Approximate Optimization Algorithm using Machine Learning. In Proceedings of the 23rd Conference on Design, Automation and Test in Europe ; Grenoble, France, March 9-13, 2020, EDA Consortium, 2020; pp 686–689.10.23919/DATE48585.2020.9116348 ↗
58. 10.1109/TQE.2023.332516710.1109/TQE.2023.3325167 ↗
59. 10.1103/PhysRevResearch.2.03344610.1103/PhysRevResearch.2.033446 ↗
60. 10.1038/s41586-024-08148-810.1038/s41586-024-08148-8 ↗
61. 10.1038/s42256-024-00927-210.1038/s42256-024-00927-2 ↗
62. 10.48550/arXiv.2501.09702010.48550/arXiv.2501.097020 ↗
63. 10.48550/arXiv.2511.0022410.48550/arXiv.2511.00224 ↗
64. 10.1126/sciadv.adu999110.1126/sciadv.adu9991 ↗
65. 10.1039/D2SC01492K10.1039/D2SC01492K ↗
66. 10.1021/acsami.3c0821410.1021/acsami.3c08214 ↗
67. 10.1049/gtd2.1260210.1049/gtd2.12602 ↗
68. 10.1016/j.joule.2024.03.02010.1016/j.joule.2024.03.020 ↗
69. 10.1016/j.energy.2019.04.18610.1016/j.energy.2019.04.186 ↗
70. 10.1186/s41313-021-00032-610.1186/s41313-021-00032-6 ↗
71. 10.1021/acs.jctc.8b0045010.1021/acs.jctc.8b00450 ↗
72. 10.1016/j.chemphys.2024.11219510.1016/j.chemphys.2024.112195 ↗
73. 10.1038/s41524-023-01045-010.1038/s41524-023-01045-0 ↗
74. 10.1140/epjqt/s40507-022-00155-w10.1140/epjqt/s40507-022-00155-w ↗
75. 10.1038/s41534-022-00618-z10.1038/s41534-022-00618-z ↗
76. 10.1103/PhysRevX.15.02105710.1103/PhysRevX.15.021057 ↗
77. 10.1038/s41534-021-00390-610.1038/s41534-021-00390-6 ↗
78. 10.1038/s42254-025-00813-910.1038/s42254-025-00813-9 ↗
79. 10.48550/arXiv.2503.0022110.48550/arXiv.2503.00221 ↗
80. 10.1007/s11128-022-03670-y10.1007/s11128-022-03670-y ↗
81. Blondel M. · Proc. Int. Conf. Neural Inf. Process. Syst. · 2016 · 3351
82. 10.1088/2058-9565/acae3e10.1088/2058-9565/acae3e ↗