CUDA-Q is a programming platform for hybrid quantum-classical systems that combines quantum processing units with GPUs and CPUs. Developers and researchers use its C++ and Python tools, compiler, runtime, and hardware backends to build and test quantum applications. The catalogue skills support development with CUDA-Q.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA/cuda-quantum --skill cudaq-importinggit clone --depth 1 https://github.com/NVIDIA/cuda-quantumWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/cuda-quantum/cudaq-importing)<a href="https://agentmods.dev/skills/nvidia/cuda-quantum/cudaq-importing"><img src="https://agentmods.dev/badge/skills/nvidia/cuda-quantum/cudaq-importing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/cuda-quantum/cudaq-importing"><img src="https://agentmods.dev/badge/skills/nvidia/cuda-quantum/cudaq-importing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.01616 |
| Opus 5.5 | $0.00014 | $0.00646 |
| Sonnet 5.5 | $0.00007 | $0.00323 |
| Haiku 4.5 | $0.00003 | $0.00162 |
Grade A, and why
cudaq-importing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to cudaq-importing — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CUDA-Q Importing
Purpose
Use this skill to port quantum circuits from another framework into CUDA-Q Python kernels. This includes Qiskit code and Qiskit-style circuit construction, as well as other framework-driven circuit builders. The goal is a framework-free CUDA-Q port that preserves the source quantum algorithm, matches source behavior at small test sizes, and documents any unavoidable CUDA-Q limitations.
For authoring new CUDA-Q kernels from scratch, and for CUDA-Q installation,
simulation targets, QPU access, and parallelization, use the cudaq-guide
skill (/cudaq-guide author for kernel authoring).
Prerequisites
- Python 3.10+.
- CUDA-Q installed in the target environment. Check the runtime with:
python -c "import cudaq; print(getattr(cudaq, '__version__', 'unknown'))". - Access to the source implementation and a way to run or inspect its expected behavior.
- To validate against the source framework (e.g. Qiskit/Aer), it must be installed in the validation environment only. The final CUDA-Q port itself must not require the source framework.
- When using CUDA-Q documentation or repository MCP connectors, verify the connector is available before relying on it; otherwise use local docs or the source tree.
- When debugging and the installed CUDA-Q version differs from the latest documentation, review relevant documentation or source changes before treating a behavior difference as a porting bug.
Workflow
- Read the source circuit construction and identify the exact algorithm, qubit/register layout, measurement behavior, and any framework helpers.
- Preserve the high-level quantum algorithm. Do not replace mid-circuit measurement, QPE structure, oracle definitions, or decomposition strategy without explicit user permission.
- Select the CUDA-Q execution pattern:
- Use
cudaq.samplefor final-measurement sampling. - Use
cudaq.runwhen mid-circuit measurement values must be returned or used per shot. - Use runtime-argument kernels instead of generated per-size kernels unless CUDA-Q requires a fixed-length return shape.
- Use
- Translate gates and subcircuits. For detailed gate mappings, ordering rules, precision guidance, and helper-extraction patterns, read references/porting-reference.md.
- Remove runtime source-framework dependencies from the CUDA-Q port. Extract pure helpers into framework-free modules.
- Validate with small deterministic inputs before scaling. Compare raw count keys and distributions, not just aggregate fidelity.
- Re-run any previously failing configurations after every fix.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed bd078654245a
- 1mo ago First seen · 165 lines · 34 tokens per session scan A b1993b02b3d0
cudaq-importing is a skill published in the GitHub repository NVIDIA/cuda-quantum (1,167 stars, last pushed today), licensed Apache-2.0. It adds 34 tokens to every session and 1,616 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to cudaq-importing, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
cudaq-importing
Use when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.
quantum-algorithms-expert
Develop production-ready quantum algorithms for optimization, simulation, and machine learning on near-term quantum devices. Use when the user mentions quantum computing, Qiskit or Cirq, quantum circuits, QAOA, VQE, Grover or Shor, or quantum machine learning on NISQ devices.
cudaq-guide
Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.
qiskit-quantum
Use this Skill for quantum computing experiments with Qiskit: qubit circuits, gates, measurement, VQE/QAOA variational algorithms, noise models, and statevector vs shot simulation.
cuopt-numerical-optimization-api
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
cuopt-developer
Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.