Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

• Published in: Future generation computer systems Vol. 160; pp. 666 - 710
• Main Authors: Alexeev, Yuri, Barroca, Marco Antonio, Bassini, Sanzio, Battelle, Torey, Camps, Daan, Casanova, David, Choi, Young Jay, Chong, Frederic T., Chung, Charles, Codella, Christopher, Córcoles, Antonio D., Di Meglio, Alberto, Eckl, Thomas, Economou, Sophia, Eidenbenz, Stephan, Fare, Clyde, Faro, Ismael, Ferreira, Rodrigo Neumann Barros, Fuji, Keisuke, Galli, Giulia, Glick, Jennifer R., Gokhale, Pranav, de la Puente Gonzalez, Salvador, Greiner, Johannes, Gropp, Bill, Grossi, Michele, Gull, Emanuel, Hermes, Matthew R., Humble, Travis S., Ito, Nobuyasu, Izmaylov, Artur F., Javadi-Abhari, Ali, Jha, Shantenu, Jones, Barbara, Jurcevic, Petar, Kirby, William, Kister, Stefan, Kitagawa, Masahiro, Klassen, Joel, Klymko, Katherine, Koh, Kwangwon, Kürkçüog̃lu, Dog̃a Murat, Kurowski, Krzysztof, Laino, Teodoro, Landfield, Ryan, Leininger, Matt, Leyton-Ortega, Vicente, Li, Ang, Lin, Meifeng, Liu, Junyu, Lorente, Nicolas, Luckow, Andre, Martiel, Simon, Martin-Fernandez, Francisco, Martonosi, Margaret, Marvinney, Claire, Medina, Arcesio Castaneda, Mezzacapo, Antonio, Michielsen, Kristel, Mittal, Tushar, Moon, Kyungsun, Mostame, Sarah, Na, Young-Hye, Nam, Yunseong, Narang, Prineha, Ottaviani, Daniele, Otten, Matthew, Pascuzzi, Vincent R., Pednault, Edwin, Piontek, Tomasz, Rall, Patrick, Ravi, Gokul Subramanian, Rossi, Matteo A.C., Rydlichowski, Piotr, Ryu, Hoon, Samsonidze, Georgy, Sato, Mitsuhisa, Saurabh, Nishant, Sharma, Vidushi, Sharma, Kunal, Shin, Soyoung, Sitdikov, Iskandar, Suh, In-Saeng, Switzer, Eric D., Tang, Wei, Thompson, Joel, Todo, Synge, Tran, Minh C., Trott, Christian, Tseng, Huan-Hsin, Tubman, Norm M., Valiñas, David García, Wever, Christopher, Wojciechowski, Konrad, Wu, Xiaodi, Yoo, Shinjae, Yu, Victor Wen-zhe, Yunoki, Seiji, Zhuk, Sergiy, Zubarev, Dmitry
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2024
• Subjects: High-performance computing; Materials science; Quantum computing; Quantum-centric supercomputing; Materials science; Quantum-centric supercomputing; Quantum computing; High-performance computing
• ISSN: 0167-739X
• DOI: 10.1016/j.future.2024.04.060

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions. •Fundamental quantum algorithms to construct quantum–classical workflows.•Classical processing to alleviate quantum workloads and deal with large data.•Classical workload management and programming models for quantum workflows.•Use cases representative of the variety of topics in materials science.