pennylane

Develop and train quantum machine learning models with automatic differentiation.

1|Updated Jan 14, 2026

Search Tags:#machine learning #autodiff #qml #quantum computing #quantum chemistry #hybrid models

Ready to run

npx skills add https://github.com/Sologa/codex-pipeline --skill pennylane-sologa

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pennylane
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/pennylane
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill pennylane-sologa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables the development and training of quantum machine learning models, quantum chemistry simulations, and hybrid quantum-classical algorithms by providing a unified interface for quantum hardware and simulators.

Core Features & Use Cases

Quantum Circuit Construction: Build and execute quantum circuits using a variety of gates and operations.
Automatic Differentiation: Train quantum circuits like neural networks using gradient-based optimization.
Hybrid Models: Seamlessly integrate quantum components into classical machine learning workflows.
Use Case: Train a quantum neural network to classify complex datasets or simulate molecular energies for drug discovery.

Quick Start

Use the pennylane skill to build a simple quantum circuit with two qubits and return the expectation value of PauliZ on the first qubit.

Frequently Asked Questions about pennylane

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build and train quantum machine learning models using automatic differentiation?▼

You can build and train quantum machine learning models by constructing quantum circuits and applying gradient-based optimization. Automatic differentiation allows you to train these circuits similarly to classical neural networks for complex datasets.

Can I integrate quantum circuits with classical machine learning frameworks like PyTorch and JAX?▼

Yes, you can create hybrid quantum-classical models by seamlessly integrating quantum components into classical machine learning workflows. The framework supports direct integration with PyTorch, JAX, and TensorFlow.

How does automatic differentiation work for quantum chemistry simulations?▼

Automatic differentiation enables gradient-based optimization for quantum chemistry simulations. This mechanism computes gradients directly through quantum circuits, allowing you to accurately simulate molecular energies for applications like drug discovery.

What is the best way to execute quantum circuits across different quantum hardware and simulators?▼

The best way to execute quantum circuits across various hardware and simulators is using a unified interface. This allows you to construct circuits with various gates and operations, then run them consistently across different quantum platforms.

Do I need a specific environment to run hybrid quantum-classical algorithms?▼

You need an environment that supports classical machine learning frameworks like PyTorch, JAX, or TensorFlow. This setup allows you to seamlessly combine quantum circuit execution with classical processing for hybrid algorithm training.