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

/ Authors

Yuri Alexeev, Maximilian Amsler, Paul G. Baity, M. A. Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jai Choi, F. Chong

and 117 more authors

Charles C. Chung, Christopher Codella, A. Córcoles, James Cruise, A. D. Meglio, Jonathan Dubois, Ivan Duran, T. Eckl, Sophia Economou, S. Eidenbenz, B. Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fern'andez, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, P. Gokhale, S. González, Johannes N. Greiner, B. Gropp, M. Grossi, Emmanuel Gull, Burns Healy, Benchen Huang, Travis S. Humble, Nobuyasu Ito, A. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, S. Jha, Liang Jiang, Barbara Jones, W. A. Jong, Petar Jurcevic, William Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, K. Klymko, Kwangwon Koh, Masaaki Kondo, D. Kurkcuoglu, K. Kurowski, T. Laino, Ryan Landfield, M. Leininger, Vicente Leyton-Ortega, Ang Li, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, S. Martiel, Francisco Martín-Fernández, M. Martonosi, C. Marvinney, Arcesio Castaneda Medina, Dirk Merten, Antonio Mezzacapo, K. Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel E. Moore, Mario Motta, Young-Hye Na, Yunseong Nam, P. Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, S. Pakin, V. Pascuzzi, Ed Penault, Tomasz Piontek, J. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall F. Robertson, Matteo Rossi, Piotr Rydlichowski, Hoon Ryu, G. Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Vidushi Sharma, Kunal Sharma, S. Shin, George Slessman, Mathias B. Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Wei Tang, Joel Thompson, S. Todo, M. Tran, Dimitar Trenev, C. Trott, H. Tseng, Esin Tureci, David Garcia Valiñas, S. Vallecorsa, Christopher Wever, Konrad Wojciechowski, Xiaodi Wu, Shinjae Yoo, Nobuyuki Yoshioka, V. Yu, Seiji Yunoki, Sergiy Zhuk, D. Zubarev

/ Abstract

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. 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.

Journal: Future Gener. Comput. Syst.

DOI: 10.1016/j.future.2024.04.060