We encourage installing Qiskit Algorithms via the pip tool (a python package manager).
pip install qiskit-algorithmspip will handle all dependencies automatically and you will always install the latest (and well-tested) version.
If you want to work on the very latest work-in-progress versions, either to try features ahead of their official release or if you want to contribute to Algorithms, then you can install from source. To do this follow the instructions in the documentation.
Some optimization algorithms require specific libraries to be run:
-
Scikit-quant, may be installed using the command
pip install scikit-quant. -
SnobFit, may be installed using the command
pip install SQSnobFit. -
NLOpt, may be installed using the command
pip install nlopt.
If you'd like to contribute to Qiskit Algorithms, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs. Please join the Qiskit Slack community and for discussion and simple questions. For questions that are more suited for a forum, we use the Qiskit tag in Stack Overflow.
Qiskit Algorithms was inspired, authored and brought about by the collective work of a team of researchers. Algorithms continues to grow with the help and work of many people, who contribute to the project at different levels. If you use Qiskit, please cite as per the provided BibTeX file.
This project uses the Apache License 2.0.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent