CUDA-Q¶
Welcome to the CUDA-Q documentation page!
CUDA-Q streamlines hybrid application development and promotes productivity and scalability in quantum computing. It offers a unified programming model designed for a hybrid setting—that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for programming in Python and in C++.
You are browsing the documentation for latest version of CUDA-Q. You can find documentation for all released versions here.
CUDA-Q is a programming model and toolchain for using quantum acceleration in heterogeneous computing architectures available in C++ and Python.
- Quick Start
- Basics
- Examples
- Applications
- Quantum Enhanced Auxiliary Field Quantum Monte Carlo
- Deutsch’s Algorithm
- Quantum Fourier Transform
- Cost Minimization
- Variational Quantum Eigensolver
- Max-Cut with QAOA
- Hadamard Test and Application
- Hybrid Quantum Neural Networks
- Molecular docking via DC-QAOA
- Noisy Simulation
- Readout Error Mitigation
- Water Molecule with Active Space (CPU vs. GPU)
- Divisive Clustering With Coresets Using CUDA-Q
- Multi-Reference Quantum Krylov Algorithm (H2 Example)
- Factoring Integers With Shor’s Algorithm
- Compiling Unitaries Using Diffusion Models
- Backends
- Installation
- Integration
- Extending
- Specifications
- API Reference
- Other Versions