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Cudaq Guide

NVIDIAOfficialCreated by CUDA-Q TeamDistributed by NVIDIA

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk
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From the author

Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.
— the description by CUDA-Q Team

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/cudaq-guide/download

  2. Unzip 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

ArgumentActionReference
installWalk through Python or C++ installation and validation.references/onboarding.md
test-programBuild and run a Bell-state kernel.references/onboarding.md
gpu-simSelect GPU, multi-GPU, tensor-network, or CPU targets.references/onboarding.md
qpuGuide provider selection and credential-safe QPU setup.references/onboarding.md
applicationsSummarize CUDA-Q application areas and notebooks.references/onboarding.md
parallelizeChoose mgpu, mqpu, async dispatch, or distributed observe.references/onboarding.md
authorAuthor 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.

Files

skill-card.md

Description: <br>

Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance. <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 <br>

Use Case: <br>

Developers and engineers use this skill for CUDA-Q quantum-classical programming setup, GPU and QPU simulation target selection, and Python @cudaq.kernel authoring guidance. <br>

Deployment Geography for Use: <br>

Global <br>

Requirements / Dependencies: <br>

Requires API Key or External Credential: [No] <br> Credential Type(s): [None] <br>

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Analysis, Configuration instructions] <br> Output Format: [Markdown with inline code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

  • Claude Code (aws/anthropic/bedrock-claude-opus-4-8) <br>

  • Codex (openai/openai/gpt-5.5) <br>

Evaluation Tasks: <br>

13 evaluation tasks (10 positive, 3 negative) with 3 attempts per task in isolated sandbox pods. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>

  • Correctness: Checks final-answer correctness against the reference answer. <br>

  • Discoverability: Checks whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • Effectiveness: Checks whether the user's goal was achieved and the expected workflow behavior was followed. <br>

  • Efficiency: Checks tool-call productivity and token efficiency. <br>

Underlying evaluation signals used in this run: <br>

  • security: Unsafe operations, secret leakage, and unauthorized access. <br>

  • accuracy: Final-answer correctness against the reference answer. <br>

  • skill_execution: Whether the expected skill was selected and the workflow executed. <br>

  • goal_accuracy: Whether the user's goal was achieved. <br>

  • behavior_check: Whether the expected workflow behavior was followed. <br>

  • skill_efficiency: Tool-call productivity. <br>

  • token_efficiency: Actual uncached prompt plus completion token usage. <br>

Evaluation Results: <br>

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall93.6%89.2%
Security100.0% → 100.0% (±0.0 points)85.7% → 92.3% (+6.6 points)
Correctness92.3% → 98.5% (+6.2 points)90.0% → 98.5% (+8.5 points)
Discoverability97.5%92.0%
Effectiveness69.2% → 79.9% (+10.7 points)66.0% → 86.1% (+20.1 points)
Efficiency91.9%77.1%

Skill Version(s): <br>

1.1.2 (source: frontmatter) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

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

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download cudaq-guide.zip