CUDA-Q

Welcome to the CUDA-Q documentation page!

CUDA-Q is a programming model and toolchain for using quantum acceleration in heterogeneous computing architectures available in C++ and Python. 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++.

The CUDA-Q ecosystem supports a wide variety of QPU hardware and high-performance simulators. It includes Python and C++ interfaces for quantum programming, compiler toolchains for optimization and hardware support and low-level QPU runtimes for detailed hardware control. CUDA-Q also provides an open, extensible logical layer for fault-tolerant fault-tolerant workloads, Quantum Error Correction (QEC) codes and QPU architecture co-design, currently in preview. See the CUDA-Q Logical for more information.

You are browsing the documentation for 0.16.0 version of CUDA-Q. You can find documentation for all released versions here.

Research Preview: CUDA-Q Pulse

CUDA-Q pulse is a new research-preview package for programming at the pulse level. It provides a Python kernel DSL, pulse and operator dialects, compiler passes, and an experimental GPU execution path. Because it is a research preview, its APIs and behavior may change incompatibly or be removed without notice, and it is not a product-supported CUDA-Q feature.

See the CUDA-Q pulse README for a quick example, build instructions, and the current scope and limitations.

Contents

• Quick Start
  • Install CUDA-Q
  • Validate your Installation
  • CUDA-Q Academic
• Basics
  • What is a CUDA-Q Kernel?
  • Building your first CUDA-Q Program
  • Running your first CUDA-Q Program
  • Troubleshooting
• Examples
  • Introduction
  • Building Kernels
  • Quantum Operations
  • Measuring Kernels
  • Visualizing Kernels
  • Executing Kernels
  • Computing Expectation Values
  • Multi-GPU Workflows
  • Optimizers & Gradients
  • Noisy Simulations
  • Pre-Trajectory Sampling with Batch Execution
  • Detector Error Models
  • Constructing Operators
  • Performance Optimizations
  • Using Quantum Hardware Providers
  • When to Use sample vs. run
  • Dynamics Examples
• Applications
• Backends
  • Circuit Simulation
  • Quantum Hardware (QPUs)
  • Dynamics Simulation
  • Cloud
• Dynamics
  • Quick Start
  • Operator
  • Time-Dependent Dynamics
  • Super-operator Representation
  • Numerical Integrators
  • Batch simulation
  • Multi-GPU Multi-Node Execution
  • Examples
• Realtime
  • Installation
  • Host API
  • Messaging Protocol
  • CPU RoCE Transport
  • Device Call Channels
• CUDA-QX
  • CUDA-Q Solvers
  • CUDA-Q QEC
• Installation
  • Local Installation
  • Data Center Installation
• Integration
  • Downstream CMake Integration
  • Combining CUDA with CUDA-Q
  • Integrating with Third-Party Libraries
• Extending
  • Compiler development
  • Add a hardware backend
  • Package & distribute a backend plugin
  • Create an NVQIR simulator
• Specifications
  • Language Specification
  • Quake Specification
• API Reference
  • C++ API
  • Python API
  • Quantum Operations
• Other Versions

Preview

• CUDA-Q Logical