Quantum SDKs
SDKs allow access to IonQ resources directly from within your code environment
While you can access IonQs resources directly via API, the majority of users use an SDK that simplifies usage and helps improve the stability of your code. As we make changes to our APIs, we also update our SDKs with the appropriate change to make sure they keep working as intended. If you were using our APIs directly, you may need to implement this yourself—but with an SDK you just need to update the package.
IonQ maintains compatibility with these SDKs, but bugs happen. If you come across a problem (or you just need some help), reach out to [email protected].
Qiskit
Qiskit is an open-source Python SDK for working with quantum computers at a variety of levels—from the “metal” itself, to pulses, gates, circuits and higher-order application areas like quantum machine learning and quantum chemistry. It has “Providers” that enable support for different vendors and the various “backends” (read: quantum computers or simulators) that they offer.
Install Qiskit and the IonQ provider with:
pip install qiskit qiskit-ionq
Learn more:
Read our Guide
Quickly get up to speed and learn the fundamentals of working with this SDK
Explore the official docs
Explore the official reference materials for complete information on every feature this SDK supports
- Additional tutorials are available including implementations of VQE and QAOA
- Report issues with the
qiskit-ionqprovider in the GitHub project
Cirq
Cirq is an open source Python framework for writing, modifying, and running programs for quantum computers. As of v0.12.0, Cirq-Ionq provides support for IonQ’s trapped-ion systems. This means that you can write quantum circuits and run them on IonQ’s trapped-ion quantum computers, all from within the Cirq framework.
- Read our Cirq guide to get started
- Google’s example notebook is also a great way to get going
- A complete reference to all of the features in the SDK is available
Pennylane
PennyLane is an open-source Python library designed for quantum machine learning (QML). It facilitates the creation, simulation, and optimization of quantum circuits, enabling seamless integration with classical machine learning frameworks such as TensorFlow and PyTorch. One of PennyLane’s key features is its ability to compute gradients of quantum circuits, a critical component for quantum machine learning models. PennyLane supports various quantum computing platforms, including IonQ, through its plugin system.
Install Qiskit and the IonQ provider with:
pip install pennylane pennylane-ionq
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