QML library

As part of the reproduction effort, a lot of interesting QML papers were found. We list all of them below, ordered by their utility towards MerLin. They are sorted alphabetically by the first author’s last name.

Introductory papers to photonic QML

• Martin Bombardelli, Gerard Fleury, Philippe Lacomme, and Bogdan Vulpescu. Foundations of photonic quantum computation. 2025. arXiv:2509.04266 [quant-ph] version: 2. URL: http://arxiv.org/abs/2509.04266, doi:10.48550/arXiv.2509.04266.

• Nicolas Heurtel, Andreas Fyrillas, Grégoire de Gliniasty, Raphaël Le Bihan, Sébastien Malherbe, Marceau Pailhas, Eric Bertasi, Boris Bourdoncle, Pierre-Emmanuel Emeriau, Rawad Mezher, Luka Music, Nadia Belabas, Benoît Valiron, Pascale Senellart, Shane Mansfield, and Jean Senellart. Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing. Quantum, 2023. arXiv:2204.00602 [quant-ph]. URL: http://arxiv.org/abs/2204.00602, doi:10.22331/q-2023-02-21-931.

• Nicolas Heurtel, Shane Mansfield, Jean Senellart, and Benoît Valiron. Strong simulation of linear optical processes. Computer Physics Communications, 2023. URL: https://www.sciencedirect.com/science/article/pii/S0010465523001935, doi:10.1016/j.cpc.2023.108848.

Reproduced papers

• Kuan-Cheng Chen, Chen-Yu Liu, Yu Shang, Felix Burt, and Kin K. Leung. Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing. 2025. arXiv:2505.08474 [quant-ph]. URL: http://arxiv.org/abs/2505.08474, doi:10.48550/arXiv.2505.08474.

• Samuel Yen-Chi Chen, Shinjae Yoo, and Yao-Lung L. Fang. Quantum Long Short-Term Memory. 2020. arXiv:2009.01783 [quant-ph]. URL: http://arxiv.org/abs/2009.01783, doi:10.48550/arXiv.2009.01783.

• Beng Yee Gan, Daniel Leykam, and Dimitris G. Angelakis. Fock State-enhanced Expressivity of Quantum Machine Learning Models. EPJ Quantum Technology, 2022. arXiv:2107.05224 [quant-ph]. URL: http://arxiv.org/abs/2107.05224, doi:10.1140/epjqt/s40507-022-00135-0.

• Tak Hur, Leeseok Kim, and Daniel K. Park. Quantum convolutional neural network for classical data classification. Quantum Machine Intelligence, 2022. arXiv:2108.00661 [quant-ph]. URL: http://arxiv.org/abs/2108.00661, doi:10.1007/s42484-021-00061-x.

• Ben Jaderberg, Lewis W. Anderson, Weidi Xie, Samuel Albanie, Martin Kiffner, and Dieter Jaksch. Quantum Self-Supervised Learning. 2022. arXiv:2103.14653 [quant-ph]. URL: http://arxiv.org/abs/2103.14653, doi:10.48550/arXiv.2103.14653.

• Sonika Johri, Shantanu Debnath, Avinash Mocherla, Alexandros Singh, Anupam Prakash, Jungsang Kim, and Iordanis Kerenidis. Nearest Centroid Classification on a Trapped Ion Quantum Computer. 2020. arXiv:2012.04145 [quant-ph]. URL: http://arxiv.org/abs/2012.04145, doi:10.48550/arXiv.2012.04145.

• Muhammad Kashif, Alberto Marchisio, and Muhammad Shafique. Computational Advantage in Hybrid Quantum Neural Networks: Myth or Reality? 2025. arXiv:2412.04991 [quant-ph]. URL: http://arxiv.org/abs/2412.04991, doi:10.48550/arXiv.2412.04991.

• Sang Hyub Kim, Jonathan Mei, Claudio Girotto, Masako Yamada, and Martin Roetteler. Quantum Large Language Model Fine-Tuning. 2025. arXiv:2504.08732 [quant-ph]. URL: http://arxiv.org/abs/2504.08732, doi:10.48550/arXiv.2504.08732.

• Hon Wai Lau, Aoi Hayashi, Akitada Sakurai, William John Munro, and Kae Nemoto. Modular quantum extreme reservoir computing. 2025. arXiv:2412.19336 [quant-ph]. URL: http://arxiv.org/abs/2412.19336, doi:10.48550/arXiv.2412.19336.

• Yanan Li, Zhimin Wang, Rongbing Han, Shangshang Shi, Jiaxin Li, Ruimin Shang, Haiyong Zheng, Guoqiang Zhong, and Yongjian Gu. Quantum Recurrent Neural Networks for Sequential Learning. 2023. arXiv:2302.03244 [quant-ph]. URL: http://arxiv.org/abs/2302.03244, doi:10.48550/arXiv.2302.03244.

• Chen-Yu Liu, Kuan-Cheng Chen, Keisuke Murota, Samuel Yen-Chi Chen, and Enrico Rinaldi. Quantum Relational Knowledge Distillation. 2025. arXiv:2508.13054 [quant-ph]. URL: http://arxiv.org/abs/2508.13054, doi:10.48550/arXiv.2508.13054.

• Sirui Lu, Lu-Ming Duan, and Dong-Ling Deng. Quantum Adversarial Machine Learning. Physical Review Research, 2020. arXiv:2001.00030 [quant-ph]. URL: http://arxiv.org/abs/2001.00030, doi:10.1103/PhysRevResearch.2.033212.

• Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, and Nathan Killoran. Transfer learning in hybrid classical-quantum neural networks. Quantum, 2020. arXiv:1912.08278 [quant-ph]. URL: http://arxiv.org/abs/1912.08278, doi:10.22331/q-2020-10-09-340.

• Martin F. X. Mauser, Solène Four, Lena Marie Predl, Riccardo Albiero, Francesco Ceccarelli, Roberto Osellame, Philipp Petersen, Borivoje Dakić, Iris Agresti, and Philip Walther. Experimental data re-uploading with provable enhanced learning capabilities. 2025. arXiv:2507.05120 [quant-ph]. URL: http://arxiv.org/abs/2507.05120, doi:10.48550/arXiv.2507.05120.

• Léo Monbroussou, Beatrice Polacchi, Verena Yacoub, Eugenio Caruccio, Giovanni Rodari, Francesco Hoch, Gonzalo Carvacho, Nicolò Spagnolo, Taira Giordani, Mattia Bossi, Abhiram Rajan, Niki Di Giano, Riccardo Albiero, Francesco Ceccarelli, Roberto Osellame, Elham Kashefi, and Fabio Sciarrino. Photonic Quantum Convolutional Neural Networks with Adaptive State Injection. 2025. arXiv:2504.20989 [quant-ph]. URL: http://arxiv.org/abs/2504.20989, doi:10.48550/arXiv.2504.20989.

• William John Munro, Akitada Sakurai, Aoi Hayashi, and Kae Nemoto. Photonic quantum extreme reservoir computation. In CLEO 2024. Optica Publishing Group, 2024. URL: https://opg.optica.org/abstract.cfm?uri=CLEO_FS-2024-FM2K.2, doi:10.1364/CLEO_FS.2024.FM2K.2.

• Akitada Sakurai, Marta P. Estarellas, William J. Munro, and Kae Nemoto. Quantum Extreme Reservoir Computation Utilizing Scale-Free Networks. Physical Review Applied, 2022. URL: https://link.aps.org/doi/10.1103/PhysRevApplied.17.064044, doi:10.1103/PhysRevApplied.17.064044.

• Akitada Sakurai, Aoi Hayashi, William John Munro, and Kae Nemoto. Quantum optical reservoir computing powered by boson sampling. Optica Quantum, 2025. URL: https://opg.optica.org/opticaq/abstract.cfm?uri=opticaq-3-3-238, doi:10.1364/OPTICAQ.541432.

• Akitada Sakurai, Aoi Hayashi, William John Munro, and Kae Nemoto. Simple Hamiltonian dynamics is a powerful quantum processing resource. 2025. arXiv:2405.14245 [quant-ph]. URL: http://arxiv.org/abs/2405.14245, doi:10.48550/arXiv.2405.14245.

• Mirela Selimović, Iris Agresti, Michał Siemaszko, Joshua Morris, Borivoje Dakić, Riccardo Albiero, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Magdalena Stobińska, and Philip Walther. Experimental neuromorphic computing based on quantum memristor. 2025. arXiv:2504.18694 [quant-ph]. URL: http://arxiv.org/abs/2504.18694, doi:10.48550/arXiv.2504.18694.

• Xin Wang, Yabo Wang, Bo Qi, and Rebing Wu. Limitations of Amplitude Encoding on Quantum Classification. 2025. arXiv:2503.01545 [quant-ph] version: 1. URL: http://arxiv.org/abs/2503.01545, doi:10.48550/arXiv.2503.01545.

• Zhenghao Yin, Iris Agresti, Giovanni de Felice, Douglas Brown, Alexis Toumi, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Bob Coecke, and Philip Walther. Experimental quantum-enhanced kernels on a photonic processor. 2024. arXiv:2407.20364 [quant-ph]. URL: http://arxiv.org/abs/2407.20364, doi:10.48550/arXiv.2407.20364.

Reproduced papers in progress

• Fong Yew Leong, Wei-Bin Ewe, Si Bui Quang Tran, Zhongyuan Zhang, and Jun Yong Khoo. Hybrid quantum physics-informed neural network: Towards efficient learning of high-speed flows. Computers & Fluids, 2025. URL: https://www.sciencedirect.com/science/article/pii/S0045793025002427, doi:10.1016/j.compfluid.2025.106782.

• Tigran Sedrakyan and Alexia Salavrakos. Photonic quantum generative adversarial networks for classical data. Optica Quantum, 2024. URL: http://arxiv.org/abs/2405.06023, doi:10.1364/OPTICAQ.530346.

Reproduced papers to do

• El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur, Jonas Landman, Martin Strahm, and Yun Yvonna Li. Quantum Vision Transformers. Quantum, 2024. arXiv:2209.08167 [quant-ph]. URL: http://arxiv.org/abs/2209.08167, doi:10.22331/q-2024-02-22-1265.

• Tak Hur, Israel F. Araujo, and Daniel K. Park. Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning. Physical Review A, 2024. arXiv:2311.11412 [quant-ph]. URL: http://arxiv.org/abs/2311.11412, doi:10.1103/PhysRevA.110.022411.

• Ben Jaderberg, Antonio A. Gentile, Atiyo Ghosh, Vincent E. Elfving, Caitlin Jones, Davide Vodola, John Manobianco, and Horst Weiss. Potential of quantum scientific machine learning applied to weather modelling. 2024. arXiv:2404.08737 [quant-ph]. URL: http://arxiv.org/abs/2404.08737, doi:10.48550/arXiv.2404.08737.

• Marco Parigi, Stefano Martina, and Filippo Caruso. Quantum-Noise-Driven Generative Diffusion Models. Advanced Quantum Technologies, 2025. arXiv:2308.12013 [quant-ph]. URL: http://arxiv.org/abs/2308.12013, doi:10.1002/qute.202300401.

• Markus Rambach, Abhishek Roy, Alexei Gilchrist, Akitada Sakurai, William J. Munro, Kae Nemoto, and Andrew G. White. Photonic Quantum-Accelerated Machine Learning. 2025. arXiv:2512.08318 [quant-ph]. URL: http://arxiv.org/abs/2512.08318, doi:10.48550/arXiv.2512.08318.

• Melvin Strobl, M. Emre Sahin, Lucas van der Horst, Eileen Kuehn, Achim Streit, and Ben Jaderberg. Fourier Fingerprints of Ansatzes in Quantum Machine Learning. 2025. arXiv:2508.20868 [quant-ph]. URL: http://arxiv.org/abs/2508.20868, doi:10.48550/arXiv.2508.20868.