merlinquantum/reproduced_papers
Benchmarking photonic QML against the wider QML landscape to reveal what truly works, what scales, and where the community can push next.
This project helps researchers and practitioners evaluate and compare different quantum machine learning techniques, especially those using photonic or optical quantum computing. It takes published research papers as input and provides reproducible code and results, showcasing what these advanced algorithms can achieve in areas like image classification, sentiment analysis, and function fitting. Scientists and quantum machine learning engineers focused on practical applications of quantum computing would find this useful.
Use this if you are a quantum machine learning researcher or engineer interested in understanding the practical performance and scalability of quantum algorithms, particularly those leveraging photonic systems, for real-world tasks.
Not ideal if you are a general machine learning practitioner looking for off-the-shelf classical models or an introduction to basic quantum computing concepts.
Stars
8
Forks
15
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 27, 2026
Commits (30d)
0
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