[PR #2919] [MERGED] add Pennylane #8941

New Issue

GiteaMirror · 2026-04-18T22:57:04-05:00

GiteaMirror commented

2026-04-18 22:57:04 -05:00

📋 Pull Request Information

Original PR: https://github.com/vinta/awesome-python/pull/2919
Author: @dakshp26
Created: 2/17/2026
Status: ✅ Merged
Merged: 2/17/2026
Merged by: @JinyangWang27

Base: master ← Head: dakshp26-add-pennylane-quantum

📝 Commits (1)

ae59607 added Pennylane library to README

📊 Changes

1 file changed (+1 additions, -0 deletions)

View changed files

📝 README.md (+1 -0)

📄 Description

Project

PennyLane

Checklist

One project per PR
PR title format: Add pennylane
Entry format: * [PennyLane](https://github.com/PennyLaneAI/pennylane) - Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry with automatic differentiation.
Description is concise and short

Why This Project Is Awesome

Which criterion does it meet? (pick one)

Industry Standard - The go-to tool for a specific use case
Rising Star - 5000+ stars in < 2 years, significant adoption
Hidden Gem - Exceptional quality, solves niche problems elegantly

Explain: PennyLane is the main open-source framework for quantum machine learning in Python. It is widely used in research for differentiable quantum circuits, hybrid quantum–classical models, and integration with PyTorch, TensorFlow, JAX, and NumPy. With ~3k GitHub stars and active development, it is the standard tool for quantum ML and differentiable quantum algorithms.

How It Differs

If similar entries exist, what makes this one unique?

Qiskit focuses on circuit-based quantum computing, transpilation, and running on IBM hardware. QuTiP focuses on quantum dynamics and open quantum systems. PennyLane focuses on quantum machine learning: automatic differentiation of quantum circuits, hybrid classical–quantum models, and integration with ML frameworks. It targets differentiable quantum algorithms and QML research, which is distinct from the other two.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/vinta/awesome-python/pull/2919 **Author:** [@dakshp26](https://github.com/dakshp26) **Created:** 2/17/2026 **Status:** ✅ Merged **Merged:** 2/17/2026 **Merged by:** [@JinyangWang27](https://github.com/JinyangWang27) **Base:** `master` ← **Head:** `dakshp26-add-pennylane-quantum` --- ### 📝 Commits (1) - [`ae59607`](https://github.com/vinta/awesome-python/commit/ae59607ad030ae74911373f3ccf10c532f0c90da) added Pennylane library to README ### 📊 Changes **1 file changed** (+1 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+1 -0) </details> ### 📄 Description ## Project [PennyLane](https://github.com/PennyLaneAI/pennylane) ## Checklist - [x] One project per PR - [x] PR title format: `Add pennylane` - [x] Entry format: `* [PennyLane](https://github.com/PennyLaneAI/pennylane) - Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry with automatic differentiation.` - [x] Description is concise and short ## Why This Project Is Awesome Which criterion does it meet? (pick one) - [x] **Industry Standard** - The go-to tool for a specific use case - [ ] **Rising Star** - 5000+ stars in < 2 years, significant adoption - [ ] **Hidden Gem** - Exceptional quality, solves niche problems elegantly **Explain:** PennyLane is the main open-source framework for quantum machine learning in Python. It is widely used in research for differentiable quantum circuits, hybrid quantum–classical models, and integration with PyTorch, TensorFlow, JAX, and NumPy. With ~3k GitHub stars and active development, it is the standard tool for quantum ML and differentiable quantum algorithms. ## How It Differs If similar entries exist, what makes this one unique? **Qiskit** focuses on circuit-based quantum computing, transpilation, and running on IBM hardware. **QuTiP** focuses on quantum dynamics and open quantum systems. **PennyLane** focuses on quantum machine learning: automatic differentiation of quantum circuits, hybrid classical–quantum models, and integration with ML frameworks. It targets differentiable quantum algorithms and QML research, which is distinct from the other two. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2026-04-18 22:57:04 -05:00

GiteaMirror closed this issue

2026-04-18 22:57:06 -05:00

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Reference: github-starred/awesome-python#8941