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Quantum Algorithms for Quantum Chemistry and Quantum Materials Science

Bauer, Bela and Bravyi, Sergey and Motta, Mario and Chan, Garnet Kin-Lic (2020) Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. Chemical Reviews, 120 (22). pp. 12685-12717. ISSN 0009-2665.

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As we begin to reach the limits of classical computing, quantum computing has emerged as a technology that has captured the imagination of the scientific world. While for many years, the ability to execute quantum algorithms was only a theoretical possibility, recent advances in hardware mean that quantum computing devices now exist that can carry out quantum computation on a limited scale. Thus, it is now a real possibility, and of central importance at this time, to assess the potential impact of quantum computers on real problems of interest. One of the earliest and most compelling applications for quantum computers is Feynman’s idea of simulating quantum systems with many degrees of freedom. Such systems are found across chemistry, physics, and materials science. The particular way in which quantum computing extends classical computing means that one cannot expect arbitrary simulations to be sped up by a quantum computer, thus one must carefully identify areas where quantum advantage may be achieved. In this review, we briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics that are of potential interest for solution on a quantum computer. We then take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal-state simulation and analyze their strengths and weaknesses for future developments.

Item Type:Article
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URLURL TypeDescription Paper
Bauer, Bela0000-0001-9796-2115
Bravyi, Sergey0000-0002-4032-470X
Motta, Mario0000-0003-1647-9864
Chan, Garnet Kin-Lic0000-0001-8009-6038
Additional Information:© 2020 American Chemical Society. Received: December 21, 2019; Published: October 22, 2020. This review was adapted and condensed from a report produced for a U.S. National Science Foundation workshop held January 22–24, 2019, in Alexandria VA. Funding for the workshop and for the production of the initial report was provided by the U.S. National Science Foundation via award no. CHE 1909531. Additional support for GKC for the production of this review from the initial report was provided by the US National Science Foundation via award no. 1839204. The workshop participants included: Garnet Kin-Lic Chan, Sergey Bravyi, Bela Bauer, Ryan Babbush, Victor Batista, Timothy Berkelbach, Tucker Carrington, Gavin Crooks, Francesco Evangelista, Joe Subotnik, Haobin Wang, Bryan Clark, Jim Freericks, Emanuel Gull, Barbara Jones, Austin Minnich, Steve White, Andrew Childs, Sophia Economou, Sabre Kais, Guang Hao Low, Antonio Mezzacapo, Daniel Crawford, Edgar Solomonik, Takeshi Yamazaki, Christopher Chang, Alexandra Courtis, Sarom Leang, Mekena Metcalf, Anurag Mishra, Mario Motta, Petr Plechac, Ushnish Ray, Julia Rice, Yuan Su, Chong Sun, Miroslav Urbanek, Prakash Verma, and Erika Ye. The workshop website is The authors declare the following competing financial interest(s): G.K.-L.C. is a cofounder and part-owner of QSimulate Inc.
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Issue or Number:22
Record Number:CaltechAUTHORS:20201022-112712261
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Official Citation:Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. Bela Bauer, Sergey Bravyi, Mario Motta, and Garnet Kin-Lic Chan. Chemical Reviews 2020 120 (22), 12685-12717; DOI: 10.1021/acs.chemrev.9b00829
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106224
Deposited By: George Porter
Deposited On:22 Oct 2020 19:15
Last Modified:25 Nov 2020 19:02

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