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.
8.3
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Machine Learning
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Excellent quantum ML skill with comprehensive coverage. The description clearly articulates when to use PennyLane (variational algorithms, hybrid models, hardware portability) vs alternatives (qiskit for IBM-specific, cirq for Google, qutip for open systems). SKILL.md provides well-structured quick starts and workflows, with detailed task knowledge systematically organized into 7 reference files covering circuits, ML integration, chemistry, devices, optimization, and advanced features. The skill addresses a genuinely novel domain—quantum machine learning with automatic differentiation—that would require extensive expertise and many tokens for a CLI agent to handle independently. Structure is exemplary: concise main file with clear signposting to specialized references. Minor point: while highly valuable for the niche quantum computing audience, the absolute novelty score reflects that this serves a specialized (though growing) field. Overall, this is a well-crafted skill that meaningfully reduces complexity for quantum ML workflows.
K-Dense-AI
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