OpenQAOA is a python open-source multi-backend Software Development Kit to create, customise and execute the Quantum Approximate Optimisation Algorithm (QAOA) on Noisy Intermediate-Scale Quantum (NISQ) devices, and simulators
Reason this release was yanked:
newer version available
Project description
OpenQAOA
A multi-backend python library for quantum optimization using QAOA on Quantum computers and Quantum computer simulators.
OpenQAOA is currently in OpenBeta.
Please, consider joining our discord if you want to be part of our community and participate in the OpenQAOA's development.
Check out OpenQAOA website https://openqaoa.entropicalabs.com/
Installation instructions
You can install the latest version of OpenQAOA directly from PyPI. First, create a virtual environment with python3.8, 3.9, 3.10 and then pip install openqaoa with the following command
pip install openqaoa
Alternatively, you can install manually directly from the GitHub repository by
- Clone the git repository:
git clone https://github.com/entropicalabs/openqaoa.git
-
Creating a python
virtual environment
for this project is recommended. (for instance, using conda). Instructions on how to create a virtual environment can be found here. Make sure to use python 3.8 (or newer) for the environment. -
After cloning the repository
cd openqaoa
and pip install the package.
pip install .
If you are interested in running the tests or the docs you can do so my using the installment modifiers [docs]
and [tests]
. For example,
pip install .[tests]
Should you face any issue during the installation, please drop us an email at openqaoa@entropicalabs.com or open an issue!
Getting started
The documentation for OpenQAOA can be found here.
We also provide a set of tutorials to get you started. Among the many, perhaps you can get started with the following ones:
- Run your first OpenQAOA workflow
- How about trying some RQAOA for a change?
- Introducing EL's fast QAOA simulator
- Discover OpenQAOA's custom parametrizations
Key Features
-
Build advanced QAOAs. Create complex QAOAs by specifying custom parametrisation, mixer hamiltonians, classical optimisers and execute the algorithm on either simulators or QPUs.
-
Recursive QAOA. Run RQAOA with fully customisable schedules on simulators and QPUs alike.
-
QPU access. Built in access for
IBM Quantum
,Rigetti QCS
, andAmazon Braket
.
Available devives
Devices are serviced both locally and on the cloud. For the IBM Quantum experience, the available devices depend on the specified credentials. For QCS and Amazon Braket, the available devices are listed in the table below:
Device location | Device Name |
---|---|
local |
['qiskit.shot_simulator', 'qiskit.statevector_simulator', 'qiskit.qasm_simulator', 'vectorized', 'pyquil.statevector_simulator'] |
Amazon Braket | IonQ, Rigetti, OQC, and simulators |
IBMQ | Please check the IBMQ backends available to your account |
Rigetti QCS | Aspen-11, Aspen-M-1, and QVM simulator |
Azure | IonQ, Quantinuum, Rigetti, QCI |
Running the tests
To run the test, first, make sure to have installed all the optional testing dependencies by running pip install .[tests]
(note, the braket must to be escaped if you are using the popular zsh shell), and then just type pytest tests/.
from the project's root folder.
:warning: Some tests require authentication: Please, check the flags in
pytest.ini
. Currently these testes are markedqpu
,api
,docker_aws
,braket_api
,sim
:warning: Some tests require Rigetti's QVM or a valid AWS Jobs docker: Please, note these tests are marked as
qvm
anddocker_aws
(see Rigetti's documentation here for more information).
Contributing and feedback
If you find any bugs or errors, have feature requests, or code you would like to contribute, feel free to open an issue or send us a pull request on GitHub.
We are always interested to hear about projects built with EntropicaQAOA. If you have an application you'd like to tell us about, drop us an email at openqaoa@entropicalabs.com
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