---

name: "cudaq-guide" title: "CUDA-Q Guide" description: "Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance." version: "1.1.2" author: "CUDA-Q Team cuda-quantum@nvidia.com" tags: [cuda-quantum, quantum-computing, onboarding, getting-started, authoring, kernels, nvidia] tools: [Read, Glob, Grep] license: "Apache-2.0" compatibility: "Python 3.10+, C++ 20" metadata: author: "CUDA-Q Team cuda-quantum@nvidia.com" tags: - cuda-quantum - quantum-computing - onboarding - getting-started - nvidia languages: - python - c++ domain: "quantum"

CUDA-Q Guide

Purpose

Guide users through CUDA-Q installation, basic kernels, GPU simulation targets, QPU access, built-in applications, multi-GPU execution, and Python @cudaq.kernel authoring. For Qiskit-to-CUDA-Q ports, route to the cudaq-importing skill instead.

Prerequisites

• Python 3.10+ for Python CUDA-Q workflows.
• CUDA Toolkit and an NVIDIA GPU for GPU-accelerated targets on Linux.
• CPU-only simulation is available through qpp-cpu; macOS is CPU-only.
• C++ workflows require Linux or WSL and C++20.
• QPU workflows require provider-specific credentials and accounts.

Instructions

• Invoke with /cudaq-guide [argument].
• If no argument is given, display the onboarding menu and ask which topic the user wants.
• Use the routing table below to choose the relevant reference file.
• Read local CUDA-Q documentation files when the answer depends on a specific CUDA-Q version or backend behavior.
• Do not answer Qiskit porting questions from this skill; use cudaq-importing.

Routing by Argument

| Argument | Action | Reference | |---|---|---| | install | Walk through Python or C++ installation and validation. | references/onboarding.md | | test-program | Build and run a Bell-state kernel. | references/onboarding.md | | gpu-sim | Select GPU, multi-GPU, tensor-network, or CPU targets. | references/onboarding.md | | qpu | Guide provider selection and credential-safe QPU setup. | references/onboarding.md | | applications | Summarize CUDA-Q application areas and notebooks. | references/onboarding.md | | parallelize | Choose mgpu, mqpu, async dispatch, or distributed observe. | references/onboarding.md | | author | Author CUDA-Q Python kernels, select execution APIs, and debug compiler issues. | references/authoring.md | | (none) | Print the menu below and ask which topic to explore. | This file |

Menu

CUDA-Q Getting Started

CUDA-Q is NVIDIA's unified quantum-classical programming model for CPUs, GPUs, and QPUs.
Supports Python and C++. Docs: https://nvidia.github.io/cuda-quantum/latest/

Choose a topic:
  /cudaq-guide install         Install CUDA-Q
  /cudaq-guide test-program    Write and run a Bell-state kernel
  /cudaq-guide gpu-sim         Accelerate simulation on NVIDIA GPUs
  /cudaq-guide qpu             Connect to real QPU hardware
  /cudaq-guide applications    Explore what you can build
  /cudaq-guide parallelize     Run across GPUs or QPUs
  /cudaq-guide author          Author @cudaq.kernel Python code

Reference Files

• references/onboarding.md: installation, test program, GPU targets, QPU providers, application areas, parallelization modes, examples, and platform troubleshooting.
• references/authoring.md: execution APIs, kernel-language constraints, silent-failure pitfalls, recurring coding patterns, resource metrics, debugging, and validation.

Limitations

• Guidance targets CUDA-Q Python/C++ workflows, with authoring details focused on decorator-mode Python APIs used in CUDA-Q 0.14 and 0.15.
• GPU and multi-GPU support depends on local CUDA-Q, CUDA Toolkit, driver, MPI, and hardware availability.
• QPU access and target options are provider-specific and may change; verify against local docs before giving operational steps.

Troubleshooting

• Import error after pip install cudaq: check Python 3.10+ and supported OS.
• No GPU detected: verify CUDA Toolkit and nvidia-smi; fall back to qpp-cpu.
• Kernel compile error: read references/authoring.md and check the restricted kernel-language subset.
• Version-specific behavior differs: compare cudaq.__version__ with the latest documentation, then review relevant documentation or source changes when debugging an installed version that is not the latest release.
• QPU submission fails: verify provider credentials are set as environment variables or through a secrets manager, never hardcoded.
• Documentation lookup fails: retry transient MCP or repository lookup once, then fall back to local docs or official CUDA-Q documentation.