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arXiv:2312.09733

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Yuri Alexeev, Maximilian Amsler, Paul Baity, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jai Choi, Frederic T. Chong, Charles Chung, Codella, Chris, Antonio D. Corcoles, Cruise, James, Alberto Di Meglio, Dubois, Jonathan, Duran, Ivan, Thomas Eckl, Sophia E. Economou, Stephan Eidenbenz, Bruce G. Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente González, Johannes Greiner, Gropp, Bill, Michele Grossi, Emanuel Gull, Burns Healy, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Liang Jiang, Barbara Jones, Wibe Albert de Jong, Petar Jurcevic, Kirby, William, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Dog̃a Murat Kürkçüog̃lu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matt Leininger, Vicente Leyton‐Ortega, Li, Ang, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel F. L. Michielsen, Abhishek Mitra, Mittal, Tushar, Kyungsun Moon, Joel E. Moore, Mário Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu‐ya Ohnishi, Ottaviani, Daniele, Matthew Otten, Scott Pakin, Vincent Richard Pascuzzi, Ed Penault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Ge. G. Samsonidze, Mitsuhisa Sato, Nishant Saurabh

Published Dec 14, 2023• 4 citations
arXivHTMLPDF

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.

DOI
10.48550/arxiv.2312.09733

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