pennylane

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.

Key Features

• Comprehensive skill evaluation and performance tracking
• Community-driven ratings and reviews
• Easy integration with Claude Code
• Regular updates and maintenance

Quick Start

TopRank Skills install K-Dense-AI/pennylane