%0 Journal Article %D 2023 %T Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions %A Yuri Alexeev %A Maximilian Amsler %A Paul Baity %A Marco Antonio Barroca %A Sanzio Bassini %A Torey Battelle %A Daan Camps %A David Casanova %A Young jai Choi %A Frederic T. Chong %A Charles Chung %A Chris Codella %A Antonio D. Corcoles %A James Cruise %A Alberto Di Meglio %A Jonathan Dubois %A Ivan Duran %A Thomas Eckl %A Sophia Economou %A Stephan Eidenbenz %A Bruce Elmegreen %A Clyde Fare %A Ismael Faro %A Cristina Sanz Fernández %A Rodrigo Neumann Barros Ferreira %A Keisuke Fuji %A Bryce Fuller %A Laura Gagliardi %A Giulia Galli %A Jennifer R. Glick %A Isacco Gobbi %A Pranav Gokhale %A Salvador de la Puente Gonzalez %A Johannes Greiner %A Bill Gropp %A Michele Grossi %A Emmanuel Gull %A Burns Healy %A Benchen Huang %A Travis S. Humble %A Nobuyasu Ito %A Artur F. Izmaylov %A Ali Javadi-Abhari %A Douglas Jennewein %A Shantenu Jha %A Liang Jiang %A Barbara Jones %A Wibe Albert de Jong %A Petar Jurcevic %A William Kirby %A Stefan Kister %A Masahiro Kitagawa %A Joel Klassen %A Katherine Klymko %A Kwangwon Koh %A Masaaki Kondo %A Doga Murat Kurkcuoglu %A Krzysztof Kurowski %A Teodoro Laino %A Ryan Landfield %A Matt Leininger %A Vicente Leyton-Ortega %A Ang Li %A Meifeng Lin %A Junyu Liu %A Nicolas Lorente %A Andre Luckow %A Simon Martiel %A Francisco Martin-Fernandez %A Margaret Martonosi %A Claire Marvinney %A Arcesio Castaneda Medina %A Dirk Merten %A Antonio Mezzacapo %A Kristel Michielsen %A Abhishek Mitra %A Tushar Mittal %A Kyungsun Moon %A Joel Moore %A Mario Motta %A Young-Hye Na %A Yunseong Nam %A Prineha Narang %A Yu-ya Ohnishi %A Daniele Ottaviani %A Matthew Otten %A Scott Pakin %A Vincent R. Pascuzzi %A Ed Penault %A Tomasz Piontek %A Jed Pitera %A Patrick Rall %A Gokul Subramanian Ravi %A Niall Robertson %A Matteo Rossi %A Piotr Rydlichowski %A Hoon Ryu %A Georgy Samsonidze %A Mitsuhisa Sato %A Nishant Saurabh %A Vidushi Sharma %A Kunal Sharma %A Soyoung Shin %A George Slessman %A Mathias Steiner %A Iskandar Sitdikov %A In-Saeng Suh %A Eric Switzer %A Wei Tang %A Joel Thompson %A Synge Todo %A Minh Tran %A Dimitar Trenev %A Christian Trott %A Huan-Hsin Tseng %A Esin Tureci %A David García Valinas %A Sofia Vallecorsa %A Christopher Wever %A Konrad Wojciechowski %A Xiaodi Wu %A Shinjae Yoo %A Nobuyuki Yoshioka %A Victor Wen-zhe Yu %A Seiji Yunoki %A Sergiy Zhuk %A Dmitry Zubarev %X

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.

%8 12/14/2023 %G eng %U https://arxiv.org/abs/2312.09733 %0 Journal Article %J Phys. Rev. Research %D 2020 %T Entanglement Bounds on the Performance of Quantum Computing Architectures %A Zachary Eldredge %A Leo Zhou %A Aniruddha Bapat %A James R. Garrison %A Abhinav Deshpande %A Frederic T. Chong %A Alexey V. Gorshkov %X

There are many possible architectures for future quantum computers that designers will need to choose between. However, the process of evaluating a particular connectivity graph's performance as a quantum architecture can be difficult. In this paper, we establish a connection between a quantity known as the isoperimetric number and a lower bound on the time required to create highly entangled states. The metric we propose counts resources based on the use of two-qubit unitary operations, while allowing for arbitrarily fast measurements and classical feedback. We describe how these results can be applied to the evaluation of the hierarchical architecture proposed in Phys. Rev. A 98, 062328 (2018). We also show that the time-complexity bound we place on the creation of highly-entangled states can be saturated up to a multiplicative factor logarithmic in the number of qubits.

%B Phys. Rev. Research %V 2 %8 9/22/2020 %G eng %U https://arxiv.org/abs/1908.04802 %N 033316 %R https://doi.org/10.1103/PhysRevResearch.2.033316 %0 Journal Article %D 2019 %T Quantum Computer Systems for Scientific Discovery %A Yuri Alexeev %A Dave Bacon %A Kenneth R. Brown %A Robert Calderbank %A Lincoln D. Carr %A Frederic T. Chong %A Brian DeMarco %A Dirk Englund %A Edward Farhi %A Bill Fefferman %A Alexey V. Gorshkov %A Andrew Houck %A Jungsang Kim %A Shelby Kimmel %A Michael Lange %A Seth Lloyd %A Mikhail D. Lukin %A Dmitri Maslov %A Peter Maunz %A Christopher Monroe %A John Preskill %A Martin Roetteler %A Martin Savage %A Jeff Thompson %A Umesh Vazirani %X

The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by co-designing full stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, and significant challenges for the development of quantum computers for science over the next 2-10 years. This document is written by a community of university, national laboratory, and industrial researchers in the field of Quantum Information Science and Technology, and is based on a summary from a U.S. National Science Foundation workshop on Quantum Computing held on October 21-22, 2019 in Alexandria, VA.

%8 12/16/2019 %G eng %U https://arxiv.org/abs/1912.07577 %0 Journal Article %D 2018 %T Unitary Entanglement Construction in Hierarchical Networks %A Aniruddha Bapat %A Zachary Eldredge %A James R. Garrison %A Abhinav Desphande %A Frederic T. Chong %A Alexey V. Gorshkov %X

The construction of large-scale quantum computers will require modular architectures that allow physical resources to be localized in easy-to-manage packages. In this work, we examine the impact of different graph structures on the preparation of entangled states. We begin by explaining a formal framework, the hierarchical product, in which modular graphs can be easily constructed. This framework naturally leads us to suggest a class of graphs, which we dub hierarchies. We argue that such graphs have favorable properties for quantum information processing, such as a small diameter and small total edge weight, and use the concept of Pareto efficiency to identify promising quantum graph architectures. We present numerical and analytical results on the speed at which large entangled states can be created on nearest-neighbor grids and hierarchy graphs. We also present a scheme for performing circuit placement--the translation from circuit diagrams to machine qubits--on quantum systems whose connectivity is described by hierarchies.

%G eng %U https://arxiv.org/abs/1808.07876