While you can access IonQ’s resources directly via API, most users choose to leverage a quantum SDK like these below to simplify their code and let them focus on what matters—doing quantum computing research and development.

The SDKs below are kept up-to-date with any changes to IonQ’s APIs, meaning you’ll rarely need to make changes or updates when IonQ adds new features or capabilities to its backends (since the SDK will handle it for you).

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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:

• Additional tutorials are available including implementations of VQE and QAOA
• Report issues with the qiskit-ionq provider in the GitHub project

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PennyLane

PennyLane is an open-source Python library designed for quantum algorithm development, quantum chemistry, and quantum machine learning (QML). It facilitates the creation, simulation, and optimization of quantum circuits, while enabling seamless integration with classical machine learning frameworks such as JAX, 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 Pennylane and the IonQ plugin with:

pip install pennylane pennylane-ionq

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qBraid

The qBraid-SDK is an open-source Python framework providing a complete, platform-agnostic quantum runtime solution. Distinguishing itself through a streamlined and highly-configurable approach to cross-platform integration, the qBraid-SDK does not adhere to a fixed circuit-building library or quantum program representation. Instead, it allows clients to dynamically register and submit quantum programs of any type compatible with the architecture of the target device. This flexibility extends to customizable pipelines for program validation, transpilation, and compilation.

Install qBraid and the IonQ provider extra with:

pip install 'qbraid[ionq]'

• Explore additional example notebooks that can be launched directly on qBraid Lab.
• Browse a complete API reference for detailed documentation on all qbraid features.
• Report issues or share feedback about the qbraid[ionq] extra in the GitHub project.

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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.

Install the Cirq IonQ module with:

pip install cirq-ionq

• 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

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CUDA-Q

NVIDIA’s CUDA Quantum (CUDA-Q) is an open-source quantum development platform with a unified programming model designed for a hybrid setting, supporting computation on GPU, CPU, and QPU resources working together. CUDA-Q integrates with various QPUs (including IonQ systems) as well as GPU-accelerated quantum simulations, and it supports programming in Python and C++.

Install CUDA-Q with:

pip install cudaq

(Depending on your operating system and environment, you might install CUDA-Q directly like this, or you might use a Docker container. See NVIDIA’s documentation for more details.)

• Learn more with [NVIDIA’s developer resources(https://developer.nvidia.com/cuda-q) and other quantum content
• Try the examples and applications in the CUDA-Q docs