Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
8.7
Rating
0
Installs
Machine Learning
Category
Excellent skill documentation for PennyLane quantum computing. The description is comprehensive and clearly explains when to invoke the skill (quantum circuits, VQE, QAOA, QML, quantum chemistry). Structure is exemplary with a concise main file and well-organized references covering all major topics. Task knowledge is outstanding with complete workflows, code examples for training classifiers and VQE, device switching patterns, and 10 best practices. The skill addresses a highly novel domain (quantum computing/ML) that would be extremely token-intensive for a CLI agent to handle from scratch, providing substantial cost savings. Minor room for improvement in description clarity around specific parameter optimization scenarios, but overall this is a very high-quality skill that enables an agent to effectively work with quantum computing tasks.
davila7
Skill Author
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