Cudaq Guide
NVIDIAOfficialCreated by CUDA-Q TeamDistributed by NVIDIA
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The CUDA-Q Guide provides comprehensive support for developers working with NVIDIA's CUDA-Q programming model. It covers the full lifecycle of quantum development, including environment setup for Python and C++, selection of simulation targets (GPU-accelerated or CPU-only), and integration with real QPU hardware. The skill also offers deep-dive assistance for authoring @cudaq.kernel code, including execution API selection, kernel-language constraints, and debugging common compilation or runtime issues.
What it does
- Walks users through CUDA-Q installation and validation on various platforms.
- Assists in selecting appropriate simulation backends, including multi-GPU and tensor-network targets.
- Provides best practices for authoring and debugging Python @cudaq.kernel code.
- Guides users through connecting to and executing jobs on real QPU hardware providers.
- Explains how to parallelize quantum workloads across GPUs or QPUs.
- Offers troubleshooting steps for common import, compilation, and execution errors.
When to use it
- When setting up a new CUDA-Q development environment on Linux, macOS, or WSL.
- When you need to write or debug a quantum kernel using the @cudaq.kernel decorator.
- When choosing between simulation backends like qpp-cpu or NVIDIA GPU-accelerated targets.
- When preparing to submit quantum circuits to external QPU hardware providers.
- When optimizing quantum workloads for multi-GPU or distributed execution.
Advantages
- Provides version-aware guidance for CUDA-Q 0.14 and 0.15 APIs.
- Centralizes documentation for both onboarding and advanced kernel authoring.
- Helps avoid common pitfalls like incorrect execution API selection or kernel-language violations.
- Streamlines the process of configuring provider-specific QPU access.
Usage examples
- “/cudaq-guide install”
- The agent provides step-by-step installation instructions for Python or C++, followed by a validation test using a Bell-state kernel.
- “/cudaq-guide author”
- The agent explains how to structure a kernel, select the correct execution API (e.g., sample vs. observe), and debug common compiler issues.
- “/cudaq-guide qpu”
- The agent initiates a dialogue to identify the target QPU technology and guides the user through secure credential configuration.
- “/cudaq-guide gpu-sim”
- The agent helps the user select the optimal simulation backend based on their hardware availability and performance requirements.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/cudaq-guide/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip cudaq-guide.zip -d .agents/skills
Documentation by CUDA-Q Team
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 codeReference 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 toqpp-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.
Files
- ATTRIBUTION.mdAdded by AutoGineer896 B
- BENCHMARK.md9.2 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md4.9 KiB
- evals/EVAL.md1.9 KiB
- evals/config.yml962 B
- evals/evals.json9.2 KiB
- references/authoring.md19.6 KiB
- references/onboarding.md8.0 KiB
- skill-card.md3.9 KiB
- skill.oms.sig5.4 KiB
LICENSE
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
The author says: Python 3.10+, C++ 20
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Attribution and licence
- Created by
- CUDA-Q Team
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/cudaq-guide · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by CUDA-Q Team or NVIDIA.
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