Hybrid computing

Orchestrate machine learning, HPC, and quantum workflows across diverse compute environments. Execute Python functions on any cloud or cluster with minimal code changes.

An open-source platform integrating QPUs, GPUs, and CPUs for hybrid quantum-classical computing. Enables QPU-agnostic development and offers GPU-accelerated simulations.

Provides a comprehensive Python SDK and command-line tools for developing applications on D-Wave quantum computers and hybrid solvers. Includes API, CLI, and package documentation.

A Python library for quantum programming using Quil.

The Quantum Device Management Interface (QDMI) is one of the core components of the Munich Quantum Software Stack (MQSS)—a sophisticated software stack to connect end users to the wide range of possible quantum devices.

Quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models. Research in quantum algorithms and applications can leverage Google’s quantum computing frameworks, all from within TensorFlow.

Next-generation software stack enabling heterogeneous quantum-classical programming, compilation, and execution

QCOR is a C++ language extension and associated compiler implementation for hybrid quantum-classical programming.

JIT compile PennyLane hybrid quantum-classical programs for significant performance gains. Supports advanced control flow, differentiation, and integrates seamlessly.

An open-source Python framework for quantum machine learning, chemistry, and computing. Built by researchers for research, it integrates quantum computation with classical ML and scientific libraries.