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.
Contents
- Quick Start
- Basics
- Examples
- Introduction
- Building Kernels
- Quantum Operations
- Measuring Kernels
- Visualizing Kernels
- Executing Kernels
- Computing Expectation Values
- Multi-Control Synthesis
- Multi-GPU Workflows
- Optimizers & Gradients
- Noisy Simulations
- Constructing Operators
- Performance Optimizations
- Using Quantum Hardware Providers
- Applications
- Bernstein-Vazirani Algorithm
- Compiling Unitaries Using Diffusion Models
- Computing Magnetization With The Suzuki-Trotter Approximation
- Cost Minimization
- Deutsch’s Algorithm
- Divisive Clustering With Coresets Using CUDA-Q
- Factoring Integers With Shor’s Algorithm
- Hybrid Quantum Neural Networks
- Max-Cut with QAOA
- Molecular docking via DC-QAOA
- Multi-reference Quantum Krylov Algorithm - \(H_2\) Molecule
- Quantum Enhanced Auxiliary Field Quantum Monte Carlo
- Quantum Fourier Transform
- Quantum Teleporation
- Quantum Volume
- Readout Error Mitigation
- Variational Quantum Eigensolver
- VQE with gradients, active spaces, and gate fusion
- Using the Hadamard Test to Determine Quantum Krylov Subspace Decomposition Matrix Elements
- Anderson Impurity Model ground state solver on Infleqtion’s Sqale
- Backends
- Dynamics
- Installation
- Integration
- Extending
- Specifications
- API Reference
- Other Versions