Skip to content
This template repository provides the boilerplate and file structure required to easily create your own PennyLane plugin.
Python Makefile
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.


PennyLane Plugin Template

This template repository provides the boilerplate and file structure required to easily create your own PennyLane plugin.

See the PennyLane Developer API documentation for more details on developing a PennyLane plugin.

Target framework is a full-stack Python library for doing things.

PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations.


  • List the features provided by the plugin here. This can include:
  • The devices made available to PennyLane, as well as any special features of the devices
  • The core PennyLane operations and observables supported
  • Any additional operations and observables provided by the plugin


Plugin Name requires both PennyLane and Target framework. It can be installed via pip:

$ python -m pip install plugin-name

Getting started

Once Plugin Name is installed, the provided Target Framework devices can be accessed straight away in PennyLane.

You can instantiate these devices for PennyLane as follows:

import pennylane as qml
dev1 = qml.device('pluginname.devicename', wires=2, additional_options=10)

These devices can then be used just like other devices for the definition and evaluation of QNodes within PennyLane. For more details, see the plugin usage guide and refer to the PennyLane documentation.


We welcome contributions - simply fork the Plugin Name 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 Target Framework.


John Smith.

If you are doing research using PennyLane, please cite our papers:

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, and Nathan Killoran. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968

Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran. Evaluating analytic gradients on quantum hardware. 2018. Phys. Rev. A 99, 032331


If you are having issues, please let us know by posting the issue on our GitHub issue tracker.


Plugin Name is free and open source, released under the Apache License, Version 2.0.

You can’t perform that action at this time.