Agent skill · research science · aiskillstore
pennylane
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.
Why this skill is useful
Provides domain-specific knowledge on quantum circuit training and integration with classical ML frameworks that the AI wouldn't reliably generate on its own.
What it needs
Requires amazon-braket-pennylane-plugin, pennylane, pennylane-cirq, pennylane-ionq, pennylane-qiskit, pennylane-rigetti installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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: For quantum hardware access, install device plugins: Quick Start Build a quantum circuit and optimize its parameters: Core Capabilities 1. Quantum Circuit Construction Build circuits with gates, measurements, and state preparation. See references/quantumcircuits.md for: Single and multi-qubit gates Controlled operations and conditional logic Mid-circuit measurements and adaptive circuits Various measurement types (expectation, probability, samples) Circuit inspection and debugging 2. Quantum Machine Learning Create hybrid quantum-classical models. See references/quantumml.md for: Integration with PyTorch, JAX, TensorFlow Quantum neural networks and variational classifiers Data encoding strategies (angle, amplitude, basis, IQP) Training hybrid models with backpropagation Transfer learning with quantum circuits 3. Quantum Chemistry Simulate molecules and compute ground state energies. See references/quantumchemistry.md for: Molecular Hamiltonian generation Variational Quantum Eigensolver (VQE) UCCSD ansatz for chemistry Geometry optimization and dissociation curves Molecular property calculations 4. Device Management Execute on simulators or quantum hardware. See references/devicesbackends.md for: Built-in simulators (default.qubit, lightning.qubit, default.mixed) Hardware plugins (IBM, Amazon Braket, Google, Rigetti, IonQ) Device selection and configuration Performance optimization and caching GPU acceleration and JIT compilation 5. Optimization Train quantum circuits with various optimizers. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/pennylane