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.
npx skills add LeonChaoX/qinyan-academic-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 Install using uv: ```bash uv pip install pennylane ``` For quantum hardware access, install device plugins: ```bash # IBM Quantum uv pip install pennylane-qiskit # Amazon Braket uv pip install amazon-braket-pennylane-plugin # Google Cirq uv pip install pennylane-cirq # Rigetti Forest uv pip install pennylane-rigetti # IonQ uv pip install pennylane-ionq ``` ## Quick Start Build a quantum circuit and optimize its parameters: ```python import pennylane as qml from pennylane import numpy as np # Create device dev = qml.device('default.qubit', wires=2) # Define quantum circuit @qml.qnode(dev) def circuit(params): qml.RX(params[0], wires=0) qml.RY(params[1], wires=1) qml.CNOT(wires=[0, 1]) return qml.expval(qml.PauliZ(0)) # Optimize parameters opt = qml.GradientDescentOptimizer(stepsize=0.1) params = np.array([0.1, 0.2], requires_grad=True) for i in range(100): params = opt.step(circuit, pa
- 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 IBM Quantum uv pip install pennylane-qiskit Amazon Braket uv pip install amazon-braket-pennylane-plugin Google Cirq uv pip install pennylane-cirq Rigetti Forest uv pip install pennylane-rigetti 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/JAX/TensorFlow. 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 LeonChaoX/qinyan-academic-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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.