SDKs allow access to IonQ resources directly from within your code environment
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).
Bugs happen. If you come across a problem (or you just need some help), reach out to [email protected].
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:
Learn more:
Quickly get up to speed and learn the fundamentals of working with this SDK on IonQ hardware
Explore the official reference materials for complete information on every feature this SDK supports
qiskit-ionq
provider in the GitHub projectPennyLane 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:
Quickly get up to speed and learn the fundamentals of working with this SDK on IonQ hardware
Check out Pennylane’s extensive library of demos (over 170!) showcasing applications across a wide variety of topics.
Explore the official reference materials for complete information on every feature this SDK supports
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:
Quickly get up to speed and learn the fundamentals of working with this SDK
Explore the official reference materials for complete information on every feature this SDK supports
qbraid
features.qbraid[ionq]
extra in the GitHub project.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:
Quickly get up to speed and learn the fundamentals of working with this SDK on IonQ hardware
Explore the official reference materials for complete information on every feature this SDK supports
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:
(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.)
Quickly get up to speed and learn the fundamentals of working with this SDK on IonQ hardware
Explore the official reference materials for complete information on every feature this SDK supports
SDKs allow access to IonQ resources directly from within your code environment
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).
Bugs happen. If you come across a problem (or you just need some help), reach out to [email protected].
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:
Learn more:
Quickly get up to speed and learn the fundamentals of working with this SDK on IonQ hardware
Explore the official reference materials for complete information on every feature this SDK supports
qiskit-ionq
provider in the GitHub projectPennyLane 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:
Quickly get up to speed and learn the fundamentals of working with this SDK on IonQ hardware
Check out Pennylane’s extensive library of demos (over 170!) showcasing applications across a wide variety of topics.
Explore the official reference materials for complete information on every feature this SDK supports
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:
Quickly get up to speed and learn the fundamentals of working with this SDK
Explore the official reference materials for complete information on every feature this SDK supports
qbraid
features.qbraid[ionq]
extra in the GitHub project.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:
Quickly get up to speed and learn the fundamentals of working with this SDK on IonQ hardware
Explore the official reference materials for complete information on every feature this SDK supports
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:
(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.)
Quickly get up to speed and learn the fundamentals of working with this SDK on IonQ hardware
Explore the official reference materials for complete information on every feature this SDK supports