5 GitHub Repositories to Learn Quantum Machine Learning

2026-04-22 · Source: KDnuggets · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, quick

Summary

Five GitHub repositories offer diverse entry points for learning quantum machine learning, a field combining quantum computing and machine learning. The "awesome-quantum-machine-learning" repository (3.2k stars) provides a comprehensive overview of subtopics like kernels and variational circuits. For deeper research, "awesome-quantum-ml" (407 stars) curates scientific papers and academic works on quantum algorithms. "Hands-On-Quantum-Machine-Learning-With-Python-Vol-1" (163 stars) offers Python code for practical, chapter-based learning. "Quantum-Machine-Learning-on-Near-Term-Quantum-Devices" (25 stars) focuses on projects for current noisy quantum hardware, including quantum support vector machines and convolutional neural networks. Finally, "qiskit-machine-learning" (939 stars), co-maintained by IBM and the Hartree Centre, is a full-featured library for building robust quantum machine learning pipelines, integrating with PyTorch via `TorchConnector`.

Key takeaway

For research scientists exploring quantum machine learning, you should adopt a structured learning path. Begin by mapping the field with comprehensive lists, then deepen your understanding through focused research papers. Transition to hands-on coding with Python notebooks and practical projects on near-term quantum devices, ultimately leveraging full-featured libraries like Qiskit for building robust pipelines and experiments.

Key insights

Open-source GitHub repositories provide structured pathways for learning and implementing quantum machine learning.

Principles

• Start broad, then specialize.
• Combine theoretical study with practical coding.
• Utilize community-driven resources.

Method

Begin with a broad "awesome" list, then delve into research papers, alternate between guided notebooks and near-term projects, and finally use a comprehensive library like Qiskit for professional workflows.

In practice

• Explore "awesome-quantum-machine-learning" for field overview.
• Use "Hands-On-Quantum-Machine-Learning-With-Python-Vol-1" for coding practice.
• Implement projects on near-term devices with "Quantum-Machine-Learning-on-Near-Term-Quantum-Devices".

Topics

• Quantum Machine Learning
• Quantum Computing
• Machine Learning Algorithms
• Qiskit Library
• Python Programming

Code references

• krishnakumarsekar/awesome-quantum-machine-learning
• artix41/awesome-quantum-ml
• quantum-machine-learning/Hands-On-Quantum-Machine-Learning-With-Python-Vol-1
• MonitSharma/Quantum-Machine-Learning-on-Near-Term-Quantum-Devices
• qiskit-community/qiskit-machine-learning

Best for: Research Scientist, AI Student, Machine Learning Engineer, AI Scientist

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• All fundamental knowledge in ML Course by Andrew NG that I noted and create into a repo github [R] — Detailed, beginner-friendly notes for Andrew Ng's ML Specialization are available on GitHub.
• Celebrating 10 years of IBM Quantum on the cloud — Early sharing and open development accelerate quantum computing's progress and real-world application.
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• Qiskit Code Migration with LLMs — Taxonomy-guided RAG with LLMs significantly enhances Qiskit code migration accuracy and reduces hallucinations.

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.