pennylane
OfficialDifferentiable, device-agnostic quantum computing.
AuthorK-Dense-AI
Version1.0.0
Installs0
System Documentation
What problem does it solve?
PennyLane enables building and training differentiable quantum circuits with device-agnostic execution, seamlessly integrating with PyTorch/JAX/TensorFlow for quantum machine learning and chemistry workflows.
Core Features & Use Cases
- Quantum circuit construction: Create gates and measurements with automatic differentiation.
- Quantum machine learning: Train quantum neural networks and variational classifiers.
- Quantum chemistry and chemistry workflows: Build molecular Hamiltonians and run VQE-type optimization.
- Device management: Run on simulators or hardware backends via plugins.
- Framework integration: Interoperate with PyTorch, JAX, and TensorFlow for hybrid models.
Quick Start
- Install: pip install pennylane
- Basic qnode example: import pennylane as qml; dev = qml.device("default.qubit", wires=2); @qml.qnode(dev) def circuit(x): qml.RX(x, 0); return qml.expval(qml.PauliZ(0))
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: pennylane Download link: https://github.com/K-Dense-AI/claude-scientific-skills/archive/main.zip#pennylane Please download this .zip file, extract it, and install it in the .claude/skills/ directory.