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 or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
npx skills add K-Dense-AI/scientific-agent-skills --skill pennylane --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PennyLane ## Overview PennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks. ## Installation PennyLane 0.45.0 requires Python 3.11 or newer. Install using uv with pinned versions for reproducible environments: ```bash uv pip install "pennylane==0.45.0" ``` For quantum hardware access, install the plugin matching the target provider. Start from a clean environment when adding or upgrading Qiskit because its dependency graph is strict. ```bash # IBM Quantum uv pip install "pennylane-qiskit==0.45.0" # Amazon Braket uv pip install "amazon-braket-pennylane-plugin==1.34.1" # Google Cirq uv pip install "pennylane-cirq==0.44.0" # Rigetti Forest uv pip install "pennylane-rigetti==0.40.0" # IonQ uv pip install "pennylane-ionq==0.45.0" # High-performance local simulators uv pip install "pennylane-lightning==0.45.0" # Catalyst JIT compilation uv pip install "pennylane-catalyst==0.15.0" ``` ## Quick Start Build a quantum circuit and optimize its parameters: ```python import pennylane as q
- Overview
- Installation
- Quick Start
- Core Capabilities
- 1. Quantum Circuit Construction
- 2. Quantum Machine Learning
- 3. Quantum Chemistry
- 4. Device Management
- 5. Optimization
- 6. Advanced Features
- Common Workflows
- Train a Variational Classifier
- Run VQE for Molecular Ground State
- Switch Between Devices
uv pip install "pennylane==0.45.0" IBM Quantum uv pip install "pennylane-qiskit==0.45.0" Amazon Braket uv pip install "amazon-braket-pennylane-plugin==1.34.1" Google Cirq uv pip install "pennylane-cirq==0.44.0" Rigetti Forest uv pip install "pennylane-rigetti==0.40.0" IonQ
What does the pennylane skill do?
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 or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill pennylane --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.