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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.11092 |
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| _version_ | 1866911602509348864 |
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| author | Notton, Cassandre Stott, Benjamin Schoeb, Philippe Walsh, Anthony Leboucher, Grégoire Espitalier, Vincent Apostolou, Vassilis Vigneux, Louis-Félix Salavrakos, Alexia Senellart, Jean |
| author_facet | Notton, Cassandre Stott, Benjamin Schoeb, Philippe Walsh, Anthony Leboucher, Grégoire Espitalier, Vincent Apostolou, Vassilis Vigneux, Louis-Félix Salavrakos, Alexia Senellart, Jean |
| contents | 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 introduce MerLin, an open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end-to-end differentiable training of quantum layers.
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 training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence.
By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross-modality comparisons, and hybrid classical-quantum workflows. The framework already implements hardware-aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a forward-looking co-design tool linking algorithms, benchmarks, and hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11092 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning Notton, Cassandre Stott, Benjamin Schoeb, Philippe Walsh, Anthony Leboucher, Grégoire Espitalier, Vincent Apostolou, Vassilis Vigneux, Louis-Félix Salavrakos, Alexia Senellart, Jean Machine Learning Programming Languages Quantum Physics 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 introduce MerLin, an open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end-to-end differentiable training of quantum layers. 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 training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence. By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross-modality comparisons, and hybrid classical-quantum workflows. The framework already implements hardware-aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a forward-looking co-design tool linking algorithms, benchmarks, and hardware. |
| title | MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning |
| topic | Machine Learning Programming Languages Quantum Physics |
| url | https://arxiv.org/abs/2602.11092 |