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What the foundations of quantum computer science teach us about chemistry

McClean, Jarrod R. and Rubin, Nicholas C. and Lee, Joonho and Harrigan, Matthew P. and O’Brien, Thomas E. and Babbush, Ryan and Huggins, William J. and Huang, Hsin-Yuan (2021) What the foundations of quantum computer science teach us about chemistry. Journal of Chemical Physics, 155 (15). Art. No. 150901. ISSN 0021-9606. doi:10.1063/5.0060367.

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With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in a chemical simulation with any computer. Some of these results even impact our understanding of chemistry in the real world. In this Perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on the one hand, this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution, in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics such as the ergodic hypothesis. However, we argue that this Perspective is not defeatist but rather helps shed light on the success of existing chemical models such as transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results, showing that data-augmented models are a more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can also extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
McClean, Jarrod R.0000-0002-2809-0509
Rubin, Nicholas C.0000-0003-3963-1830
Lee, Joonho0000-0002-9667-1081
Harrigan, Matthew P.0000-0001-9412-0553
Babbush, Ryan0000-0001-6979-9533
Huggins, William J.0000-0003-2735-1380
Huang, Hsin-Yuan0000-0001-5317-2613
Additional Information:© 2021 Published under an exclusive license by AIP Publishing. Submitted: 17 June 2021; Accepted: 20 September 2021; Published Online: 15 October 2021. We thank Nathan Wiebe for helpful discussions and feedback on the draft. The authors have no conflicts to disclose. Data Availability: Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Group:Institute for Quantum Information and Matter
Issue or Number:15
Record Number:CaltechAUTHORS:20211105-174820514
Persistent URL:
Official Citation:Jarrod R. McClean, Nicholas C. Rubin, Joonho Lee, Matthew P. Harrigan, Thomas E. O’Brien, Ryan Babbush, William J. Huggins, and Hsin-Yuan Huang, "What the foundations of quantum computer science teach us about chemistry", J. Chem. Phys. 155, 150901 (2021)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111769
Deposited By: Tony Diaz
Deposited On:05 Nov 2021 18:59
Last Modified:05 Nov 2021 18:59

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