Agent skill · nvidia
cudaq-importing
Use when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.
What it needs
About 4k tokens when loaded.
What this skill does
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 1. Read the source circuit construction and identify the exact algorithm, qubit/register layout, measurement behavior, and any framework helpers. 2. Preserve the high-level quantum algorithm. Do not replace mid-circuit measurement, QPE structure, oracle definitions, or decomposition strategy without explicit user permission. 3. Select the CUDA-Q execution pattern: Use cudaq.sample for final-measurement sampling. Use cudaq.run when mid-circuit measurement values must be returned or used per shot. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/cudaq-importing