The PennyLane-SF plugin integrates the StrawberryFields photonic quantum computing framework with PennyLane’s quantum machine learning capabilities.
PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations.
Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing photonic quantum circuits.
The plugin documentation can be found here: PennyLane-Strawberry Fields.
Features
Provides two devices to be used with PennyLane: strawberryfields.fock and strawberryfields.gaussian. These provide access to the Strawberry Fields Fock and Gaussian backends respectively.
Supports all core PennyLane operations and observables across the two devices.
Combine Strawberry Fields optimized simulator suite with PennyLane’s automatic differentiation and optimization.
Installation
Installation of PennyLane-SF, as well as all required Python packages, can be installed via pip:
$ python -m pip install pennylane-sf
Make sure you are using the Python 3 version of pip.
Alternatively, you can install PennyLane-SF from the source code by navigating to the top directory and running
$ python setup.py install
Dependencies
PennyLane-SF requires the following libraries be installed:
Python >=3.6
as well as the following Python packages:
PennyLane >=0.7
StrawberryFields >=0.11.2
If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.
Software tests
To ensure that PennyLane-SF is working correctly after installation, the test suite can be run by navigating to the source code folder and running
$ make test
Documentation
To build the HTML documentation, go to the top-level directory and run
$ make docs
The documentation can then be found in the doc/_build/html/ directory.
Contributing
We welcome contributions - simply fork the PennyLane-SF repository, and then make a pull request containing your contribution. All contributers to PennyLane-SF will be listed as authors on the releases.
We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane and Strawberry Fields.
Support
Source Code: https://github.com/XanaduAI/pennylane-sf
Issue Tracker: https://github.com/XanaduAI/pennylane-sf/issues
PennyLane Forum: https://discuss.pennylane.ai
If you are having issues, please let us know by posting the issue on our Github issue tracker, or by asking a question in the forum.
We also have a Strawberry Fields Slack channel - come join the discussion and chat with our Strawberry Fields team.
License
PennyLane-SF is free and open source, released under the Apache License, Version 2.0.
3 maintainers
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file PennyLane-SF-0.9.0.tar.gz.
File metadata
- Download URL: PennyLane-SF-0.9.0.tar.gz
- Upload date:
- Size: 9.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.1.post20200323 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
8a5650419e0bbdd76172d2f281fd3bf7779fbac9748ab105e1fd4d899d6075ef
|
|
| MD5 |
2301c443d4128e7c9e484c4fcf22990e
|
|
| BLAKE2b-256 |
e93ea72045a1b271cf52e176f04164577ec557c11e1134a12b49796adbbe46c0
|
File details
Details for the file PennyLane_SF-0.9.0-py3-none-any.whl.
File metadata
- Download URL: PennyLane_SF-0.9.0-py3-none-any.whl
- Upload date:
- Size: 17.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.1.post20200323 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f9ed015e16e975d6d131e9b422c2c584f4e90dba5a8191578b165316a86bceca
|
|
| MD5 |
592b219b0861d414fbcd32581a35e287
|
|
| BLAKE2b-256 |
cf9a45834c191511668c9b9d0dd3d2840acaccbee9c210011c88905e577615d4
|
