{
  "id": "scientific/pennylane",
  "version": "1.0.0",
  "name": "pennylane",
  "description": "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. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.",
  "domain": "scientific",
  "source": "external",
  "tools": [],
  "exitStates": [
    "success",
    "failure"
  ],
  "inputSchema": {},
  "outputSchema": {},
  "temperature": 0,
  "tags": [
    "scientific",
    "imported",
    "community"
  ],
  "platforms": {
    "claude": {
      "license": "Apache-2.0 license",
      "triggers": [
        "help with pennylane",
        "training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across ibm/google/rigetti/ionq"
      ]
    },
    "cursor": {},
    "skeneflow": {}
  }
}
