PennyLane plugin for IonQ
The PennyLane-IonQ plugin provides the ability to use IonQ’s ion-trap quantum computing backends with PennyLane.
PennyLane provides open-source tools for quantum machine learning, quantum computing, quantum chemistry, and hybrid quantum-classical computing.
IonQ is a ion-trap quantum computing company offering access to quantum computing devices over the cloud.
The plugin documentation can be found here.
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.
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.
To ensure that PennyLane-IonQ is working correctly after installation, the test suite can be run by navigating to the source code folder and running
$ make test
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.
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', 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.
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.
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
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.
PennyLane-IonQ is free and open source, released under the Apache License, Version 2.0.
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