Journal of Materials & Metallurgical Engineering Review Article
Quantum Computing for Crystal Design: Accelerating the Discovery and Optimization of Advanced Functional Materials
Abstract
Quantum computing is emerging as a transformative computational paradigm capable of solving complex quantum mechanical problems that remain computationally prohibitive for classical computers. Crystal design, which involves the prediction, optimization, and characterization of crystalline materials with tailored physical and chemical properties, relies heavily on accurate electronic structure calculations. Conventional computational approaches such as Density Functional Theory (DFT), Molecular Dynamics (MD), and Monte Carlo simulations provide valuable insights but face significant limitations when dealing with strongly correlated electron systems, large crystal lattices, and high-dimensional optimization problems. Quantum algorithms such as the Variational Quantum Eigen solution (VQE), Quantum Phase Estimation (QPE), Quantum Approximate Optimisation Algorithm (QAOA), and Quantum machine learning (QML) provide intriguing alternatives. These methods enable more efficient calculations of molecular energies, electronic band structures, crystal defects, lattice dynamics, and materials optimization. This review presents a comprehensive overview of quantum computing applications in crystal design, highlighting recent developments in quantum hardware, hybrid quantum-classical algorithms, and AI-assisted materials discovery. The paper discusses quantum computational methods for crystal structure prediction, defect engineering, electronic property evaluation, and high-throughput materials screening. Furthermore, challenges such as quantum decoherence, limited qubit availability, error correction, scalability, and software development are critically examined. Finally, future research directions involving fault-tolerant quantum computers, digital twins, autonomous laboratories, and quantum-enhanced generative AI for crystal engineering are explored. Quantum computing is expected to revolutionize computational materials science by significantly reducing computational cost while improving prediction accuracy for next-generation functional materials.
Keywords
References (50)
- Desiraju GR. Crystal engineering: A holistic view. Angew Chem Int Ed Engl. 2007;46(44):8342–8356. doi:10.1002/anie.200700534.
- Aakeröy CB, Salmon DJ. Building co-crystals with molecular sense and supramolecular sensibility. CrystEngComm. 2005 Jul 5;7(72):439-48.
- Niyonzima FN, Ezechiel N, Iyamuremye A, Nkurunziza JB, Izere AN, Karegeya C. Evaluation of teachers' skills and their current level of using atomic model kits in teaching chemical bonding and atomic structure in gasabo district of rwanda. Australian Journal of Teacher Education (Online). 2025 Dec;50(4):105-24.
- Oganov AR, editor. Modern methods of crystal structure prediction. John Wiley & Sons; 2011 Aug 4.
- Hohenberg P, Kohn W. Inhomogeneous electron gas. Physical review. 1964 Nov 9;136(3B):B864.
- Kohn W, Sham LJ. Self-consistent equations including exchange and correlation effects. Phys Rev. 1965;140:A1133–A1138. doi:10.1103/PhysRev.140.A1133.
- Hafner J. Ab initio simulations of materials using density functional theory. J Comput Chem. 2008;29(13):2044–2078. doi:10.1002/jcc.21057.
- Monaco HL, Viterbo D, Scordari F, Gilli G, Zanotti G, Catti M. Fundamentals of crystallography. Giacovazzo C, editor. Oxford: Oxford university press; 2002 Jan.
- Jones RO. Density functional theory: Its origins, rise to prominence, and future. Reviews of modern physics. 2015 Jul 1;87(3):897-923.
- Cheng AH, Ser CT, Skreta M, Guzman-Cordero A, Thiede L, Burger A, Aldossary A, Leong SX, Pablo-Garcia S, Strieth-Kalthoff F, Aspuru-Guzik A. Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science. Faraday Discussions. 2025 Jan 1;256:10-60.
- Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5:83. doi:10.1038/s41524-019-0221-0.
- Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics. npj Comput Mater. 2017;3:54. doi:10.1038/s41524-017-0056-5.
- Nielsen MA, Chuang IL. Quantum Computation and Quantum Information. Cambridge University Press; 2010.
- Preskill J. Quantum computing in the NISQ era and beyond. 2018;2:79. doi:10.22331/q-2018-08-06-79.
- Cao Y, Romero J, Olson JP, et al. Quantum chemistry in the age of quantum computing. Chem Rev. 2019;119(19):10856–10915. doi:10.1021/acs.chemrev.8b00803.
- McArdle S, Endo S, Aspuru-Guzik A, Benjamin SC, Yuan X. Quantum computational chemistry. Rev Mod Phys. 2020;92:015003. doi:10.1103/RevModPhys.92.015003.
- Cerezo M, Arrasmith A, Babbush R, et al. Variational quantum algorithms. Nat Rev Phys. 2021; 3:625–644. doi:10.1038/s42254-021-00348-9.
- Aspuru-Guzik A, Dutoi AD, Love PJ, Head-Gordon M. Simulated quantum computation of molecular energies. 2005;309(5741):1704–1707. doi:10.1126/science.1113479.
- Feynman RP. Simulating physics with computers. Int J Theor Phys. 1982;21:467–488. doi:10.1007/BF02650179.
- Outeiral C, Strahm M, Shi J, et al. The prospects of quantum computing in computational molecular biology. WIREs Comput Mol Sci. 2021;11:e1481. doi:10.1002/wcms.1481.
- Zlokapa A, Villalonga B, Boixo S, Lidar DA. Boundaries of quantum supremacy via random circuit sampling. npj Quantum Information. 2023 Apr 11;9(1):36.
- Head-Gordon M, Artacho E. Chemistry on the quantum computer. Physics Today. 2023;76(4):38–44.
- Xi B, Wang H, Sun G, Zhang B, Mao R, Ge Y, Wang Y, Zhang J, Pan Y, Zhou F, Wang Y. Pocket-based molecule generation with an SE (3)-equivariant language model leads to a potent and selective HPK1 inhibitor with in vivo efficacy. bioRxiv. 2025 Sep 25:2025-09.
- Berry DW, Wan K, Baczewski AD, Eklund EC, Tikku A, Babbush R. Quantum simulation of electronic structure via quantum fast multipole method. PRX Quantum. 2026 Aug 1;7(3):033025.
- Camino B, Buckeridge J, Warburton PA, Kendon V, Woodley SM. Quantum computing and materials science: A practical guide to applying quantum annealing to the configurational analysis of materials. Journal of Applied Physics. 2023 Jun 14;133(22).
- Gould H, Tobochnik J, Meredith DC, Koonin SE, McKay SR, Christian W. An introduction to computer simulation methods: applications to physical systems. Computers in Physics. 1996 Jul 1;10(4):349.
- Shor PW. Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science. p.124–134. doi:10.1109/SFCS.1994.365700.
- Grover LK. A fast quantum mechanical algorithm for database search. Proc 28th Annu ACM Symp Theory Comput. 1996;212–219. doi:10.1145/237814.237866.
- Peruzzo A, McClean J, Shadbolt P, et al. A variational eigenvalue solver on a quantum processor. Nat Commun. 2014;5:4213. doi:10.1038/ncomms5213.
- Farhi E, Goldstone J, Gutmann S. A quantum approximate optimization algorithm. arXiv:1411.4028. 2014.
- Bauer B, Bravyi S, Motta M, Chan GKL. Quantum algorithms for quantum chemistry and quantum materials science. Chem Rev. 2020;120:12685–12717.
- Arute F, Arya K, Babbush R, et al. Quantum supremacy using a programmable superconducting processor. 2019;574:505–510. doi:10.1038/s41586-019-1666-5.
- McClean JR, Romero J, Babbush R, Aspuru-Guzik A. The theory of variational hybrid quantum-classical algorithms. New J Phys. 2016;18:023023. doi:10.1088/1367-2630/18/2/023023.
- Oganov AR, Glass CW. Crystal structure prediction using evolutionary algorithms. J Chem Phys. 2006;124:244704. doi:10.1063/1.2210932.
- Pickard CJ, Needs RJ. Ab initio random structure searching. J Phys Condens Matter. 2011;23:053201. doi:10.1088/0953-8984/23/5/053201.
- Genoni A, Macchi P. Quantum crystallography in the last decade: Developments and outlooks. 2020;10:473. doi:10.3390/cryst10060473.
- Scappucci G, Taylor PJ, Williams JR, et al. Crystalline materials for quantum computing: Semiconductor heterostructures and topological insulators exemplars. MRS Bull. 2021;46:596–606. doi:10.1557/s43577-021-00147-8.
- Schmehr JL, Wilson SD. Active crystal growth techniques for quantum materials. Annu Rev Mater Res. 2017;47:153–174. doi:10.1146/annurev-matsci-070616-124006.
- Cazorla C, Boronat J. Simulation and understanding of quantum crystals. Rev Mod Phys. 2017;89:035003.
- Giustino F. Electron–phonon interactions from first principles. Rev Mod Phys. 2017;89:015003. doi:10.1103/RevModPhys.89.015003.
- Brown KR, Chiaverini J, Sage JM, Häffner H. Materials challenges for trapped-ion quantum computers. Nat Rev Mater. 2021;6:892–905.
- Kresse G, Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B. 1996;54:11169–11186.
- Blöchl PE. Projector augmented-wave method. Phys Rev B. 1994;50:17953–17979.
- Monkhorst HJ, Pack JD. Special points for Brillouin-zone integrations. Phys Rev B. 1976;13:5188–5192.
- Tersoff J, Hamann DR. Theory and application for the scanning tunneling microscope. Phys Rev Lett. 1983;50:1998–2001.
- Binnig G, Quate CF, Gerber C. Atomic force microscope. Phys Rev Lett. 1986;56:930–933.
- Ferrari AC, Basko DM. Raman spectroscopy as a versatile tool for studying carbon materials. Nat Nanotechnol. 2013;8:235–246.
- Briggs D, Grant JT. Surface Analysis by Auger and X-ray Photoelectron Spectroscopy. IM Publications; 2003.
- Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. 2018; 559:547–555. doi:10.1038/s41586-018-0337-2.
- Pilania G. Machine learning in materials science: From explainable predictions to autonomous design. Computational Materials Science. 2021 Jun 1;193:110360.