Abstract

We develop and implement automated methods for optimizing quantum circuits of the size and type expected in quantum computations that outperform classical computers. We show how to handle continuous gate parameters and report a collection of fast algorithms capable of optimizing large-scale quantum circuits. For the suite of benchmarks considered, we obtain substantial reductions in gate counts. In particular, we provide better optimization in significantly less time than previous approaches, while making minimal structural changes so as to preserve the basic layout of the underlying quantum algorithms. Our results help bridge the gap between the computations that can be run on existing hardware and those that are expected to outperform classical computers.

Details

Title
Automated optimization of large quantum circuits with continuous parameters
Author
Nam, Yunseong 1 ; Ross, Neil J 2 ; Su, Yuan 3 ; Childs, Andrew M 3   VIAFID ORCID Logo  ; Maslov, Dmitri 4   VIAFID ORCID Logo 

1  Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA; Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, USA; IonQ, Inc., College Park, MD, USA 
2  Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA; Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, USA; Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada 
3  Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA; Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, USA; Department of Computer Science, University of Maryland, College Park, MD, USA 
4  Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA; Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, USA; National Science Foundation, Alexandria, VA, USA 
Pages
1-12
Publication year
2018
Publication date
May 2018
Publisher
Nature Publishing Group
e-ISSN
20566387
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41534-018-0072-4
ProQuest document ID
2037029207
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.