AI RESEARCH
MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning
arXiv CS.LG
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ArXi:2602.11092v2 Announce Type: replace Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We MerLin is designed around systematic benchmarking and reproducibility. As an initial contribution, we reproduce eighteen state-of-the-art photonic and hybrid QML works spanning kernel methods, reservoir computing, convolutional and recurrent architectures, generative models, and modern.