Authors: 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 | C. Codella | Antonio Córcoles | James Cruise | Alberto Di Meglio | Jonathan Dubois | I. Ďuran | 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 Gonzalez | Johannes Greiner | William Gropp | 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 A. de Jong | Petar Jurcevic | William Kirby | 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 | An‐Ping Li | Meifeng Lin | Junyu Liu | Nicolás Lorente | André Luckow | Simon Martiel | Francisco Martín-Fernández | Margaret Martonosi | Claire Marvinney | Arcesio Castaneda Medina | Dirk Merten | Antonio Mezzacapo | Kristel Michielsen | Abhishek Mitra | Tushar Mittal | Kyungsun Moon | Joel E. Moore | Mário Motta | Young-Hye Na | Yunseong Nam | Prineha Narang | Yu‐ya Ohnishi | Diego Ottaviani | Matthew Otten | Scott Pakin | V. R. Pascuzzi | Ed Penault | Tomasz Piontek | Jed W. Pitera | Patrick Rall | Gokul Subramanian Ravi | Niall F. Robertson | Matteo A. C. Rossi | Piotr Rydlichowski | Hoon Ryu | Ge. G. Samsonidze | Mitsuhisa Sato | Nishant Saurabh
Source: arXiv (Cornell University) (preprint, 2023-12-14)
Field: Materials Science — Materials Chemistry

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions 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. Supercomputer ; Computer science ; Perspective (graphical) ; Quantum computer ; Computational science ; Data science ; Identification (biology) ; Computational model ; Quantum ; Face (sociological concept) ; Parallel computing ; Simulation ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices