Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow.
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill pennylanenpx skills add K-Dense-AI/scientific-agent-skills --skill pennylane -a claude-code -gnpx skills add K-Dense-AI/scientific-agent-skills --skill pennylane -a codex -ggemini skills install https://github.com/K-Dense-AI/scientific-agent-skills.git --path scientific-skills/pennylanegh skill install K-Dense-AI/scientific-agent-skills pennylanegit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/K-Dense-AI__scientific-agent-skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/K-Dense-AI__scientific-agent-skills/scientific-skills/pennylane ~/.claude/skills/pennylane # Claude Code
cp -R /tmp/K-Dense-AI__scientific-agent-skills/scientific-skills/pennylane ~/.agents/skills/pennylane # Codex, Cursor, Gemini CLI, Copilot, OpenCode