Abstract
Understanding the best known parameters, performance, and systematic behavior of the Quantum Approximate Optimization Algorithm (QAOA) remain open research questions, even as the algorithm gains popularity. We introduce QAOAKit, a Python toolkit for the QAOA built for exploratory research. QAOAKit is a unified repository of preoptimized QAOA parameters and circuit generators for common quantum simulation frameworks. We combine, standardize, and cross-validate previously known parameters for the MaxCut problem, and incorporate this into QAOAKit. We also build conversion tools to use these parameters as inputs in several quantum simulation frameworks that can be used to reproduce, compare, and extend known results from various sources in the literature. We describe QAOAKit and provide examples of how it can be used to reproduce research results and tackle open problems in quantum optimization.
| Original language | English |
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| Title of host publication | Proceedings of QCS 2021 |
| Subtitle of host publication | 2nd International Workshop on Quantum Computing Software, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 64-71 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781728186740 |
| DOIs | |
| State | Published - 2021 |
| Event | 2nd International Workshop on Quantum Computing Software, QCS 2021 - St. Louis, United States Duration: Nov 15 2021 → … |
Publication series
| Name | Proceedings of QCS 2021: 2nd International Workshop on Quantum Computing Software, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis |
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Conference
| Conference | 2nd International Workshop on Quantum Computing Software, QCS 2021 |
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| Country/Territory | United States |
| City | St. Louis |
| Period | 11/15/21 → … |
Funding
RS was supported by the U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research AIDE-QC and FAR-QC projects and by the Argonne LDRD program under contract number DE-AC02-06CH11357. JW was supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0068, and NSF STAQ project (PHY1818914). PCL was supported by the Defense Advanced Research Project Agency through DOE project 1868-Z361-20. KM was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1746045.
Keywords
- open quantum software
- quantum approximate optimization algorithm