PennyLane-IonQ 0.34.0

Features

• Provides two devices which can be used with IonQ’s online API: "ionq.simulator" and "ionq.qpu". These provide access to an ideal ion-trap simulator as well as IonQ’s quantum hardware, respectively.

• The plugin provides additional support for the IonQ’s Ising-type gates.

• Supports core PennyLane operations such as qubit rotations, Hadamard, basis state preparations, etc.

Installation

PennyLane-IonQ only requires PennyLane for use, no additional external frameworks are needed. The plugin can be installed via pip:

$ python3 -m pip install pennylane-ionq

Alternatively, you can install PennyLane-IonQ from the source code by navigating to the top directory and running

$ python3 setup.py install

If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.

$ make test

$ make docs

Getting started

Once PennyLane is installed, the provided IonQ devices can be accessed straight away in PennyLane. However, the user will need access credentials for the IonQ platform in order to use these remote devices. These credentials should be provided to PennyLane via a configuration file or environment variable. Specifically, the variable IONQ_API_KEY must contain a valid access key for IonQ’s online platform.

You can instantiate the IonQ devices for PennyLane as follows:

import pennylane as qml
dev1 = qml.device('ionq.simulator', wires=2, shots=1000)
dev2 = qml.device('ionq.qpu', backend='aria-1', wires=2, shots=1000)

These devices can then be used just like other devices for the definition and evaluation of quantum circuits within PennyLane. For more details and ideas, see the PennyLane website and refer to the PennyLane documentation.

Contributing

We welcome contributions-simply fork the PennyLane-IonQ repository, and then make a pull request containing your contribution. All contributers to PennyLane-IonQ will be listed as contributors 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 IonQ.

Contributors

PennyLane-IonQ is the work of many contributors.

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

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed, Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer, Zeyue Niu, Antal Száva, 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

Support

• Source Code: https://github.com/PennyLaneAI/pennylane-ionq

• Issue Tracker: https://github.com/PennyLaneAI/pennylane-ionq/issues

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

License

PennyLane-IonQ is free and open source, released under the Apache License, Version 2.0.