Published August 2, 2023
| Version v1
Software
Open
Code from "Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem"
Creators
-
Shaydulin, Ruslan1
-
Li, Changhao1
-
Chakrabarti, Shouvanik1
-
DeCross, Matthew2
-
Herman, Dylan1
-
Kumar, Niraj1
-
Larson, Jeffrey3
-
Lykov, Danylo1
-
Minssen, Pierre1
-
Sun, Yue1
-
Alexeev, Yuri3
- Dreiling, Joan M.2
- Gaebler, John P.2
- Gatterman, Thomas M.2
- Gerber, Justin A.2
- Gilmore, Kevin2
- Gresh, Dan2
- Hewitt, Nathan2
- Horst, Chandler V.2
-
Hu, Shaohan1
- Johansen, Jacob2
- Matheny, Mitchell2
- Mengle, Tanner2
- Mills, Michael2
- Moses, Steven A.2
- Neyenhuis, Brian2
- Siegfried, Peter2
-
Yalovetzky, Romina1
-
Pistoia, Marco1
- 1. JPMorgan Chase
- 2. Quantinuum
- 3. Argonne National Laboratory
Description
Code used to generate the results in the paper "Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem". See https://github.com/jpmorganchase/QOKit for latest version.
Files
QOKit.zip
Files
(12.9 MB)
Name | Size | Download all |
---|---|---|
md5:46b2a7269fd7aeb6663d44059a7f93fd
|
12.9 MB | Preview Download |
Additional details
Related works
- Is supplement to
- arXiv:2308.02342 (arXiv)
Software
- Repository URL
- https://github.com/jpmorganchase/QOKit