Published in NeurIPS 2019, 2019
Quantum version of k-means clustering algorithm with exponential speedup.
Recommended citation: Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash. (2019). "Q-means: A Quantum Algorithm for Unsupervised Machine Learning." Proceedings of NeurIPS 2019.
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Published in ICLR 2020, 2020
Quantum algorithms for implementing deep convolutional neural networks.
Recommended citation: Iordanis Kerenidis, Jonas Landman, Anupam Prakash. (2020). "Quantum Algorithms for Deep Convolutional Neural Networks." Proceedings of ICLR 2020.
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Published in Physical Review A, 2020
Quantum algorithm for spectral clustering with exponential speedup.
Recommended citation: Iordanis Kerenidis, Jonas Landman. (2020). "Quantum Spectral Clustering." Physical Review A, Vol.103 Iss. 4.
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Published in PhD Thesis, 2021
PhD Thesis on quantum algorithms for machine learning applications.
Recommended citation: Jonas Landman. (2021). "Quantum Algorithms for Unsupervised Machine Learning and Neural Networks." PhD Thesis.
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Published in AABI 2022, 2022
Bayesian approach to quantum neural networks for uncertainty quantification.
Recommended citation: Noah Berner, Vincent Fortuin, Jonas Landman. (2022). "Quantum Bayesian Neural Networks." AABI 2022.
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Published in Quantum, 2022
Quantum algorithms for neural networks with applications in medical image classification.
Recommended citation: Jonas Landman, Natansh Mathur, Yun Y Li, Martin Strahm, Skander Kazdaghli, Anupam Prakash, Iordanis Kerenidis. (2022). "Quantum Methods for Neural Networks and Application to Medical Image Classification." Quantum, vol.6, p.881.
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Published in ICLR 2023, 2023
Classical approximation of variational quantum machine learning using random Fourier features.
Recommended citation: Jonas Landman*, Slimane Thabet*, Constantin Dalyac, Hela Mhiri, Elham Kashefi. (2023). "Classically Approximating Variational Quantum Machine Learning with Random Fourier Features." Proceedings of ICLR 2023.
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Published in Quantum Science & Technology, 2024
Quantum algorithms for solving parametric partial differential equations using Fourier networks.
Recommended citation: Nishant Jain*, Jonas Landman*, Natansh Mathur, Iordanis Kerenidis. (2024). "Quantum Fourier Networks for Solving Parametric PDEs." Quantum Science & Technology, vol.9, n.3.
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Published in Quantum, 2024
Quantum implementation of vision transformers for image processing tasks.
Recommended citation: El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur, Jonas Landman, Martin Strahm, Yun Y Li. (2024). "Quantum Vision Transformers." Quantum, vol.8, p.1265.
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Published in arXiv preprint, 2024
Theoretical framework for understanding disagreements between quantum and classical machine learning models.
Recommended citation: Slimane Thabet, Léo Monbroussou, Eliott Z. Mamon, Jonas Landman. (2024). "When Quantum and Classical Models Disagree: Learning Beyond Mean Norm Least Square." arXiv preprint.
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Published in Quantum Science and Technology, 2025
Novel architectures for quantum convolutional neural networks that preserve subspace structure.
Recommended citation: Léo Monbroussou, Jonas Landman, Letao Wang, Alex B. Grilo, Elham Kashefi. (2025). "Subspace Preserving Quantum Convolutional Neural Network Architectures." Quantum Science and Technology.
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Published in Quantum, 2025
Analysis of trainability and expressivity properties of Hamming-weight preserving quantum circuits.
Recommended citation: Léo Monbroussou, Eliott Z. Mamon, Jonas Landman, Alex B. Grilo, Romain Kukla, Elham Kashefi. (2025). "Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning." Quantum, vol.9, p.1745.
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Published in Quantum, 2025
Investigation of expressivity limitations in quantum Fourier models.
Recommended citation: Hela Mhiri, Leo Monbroussou, Mario H Gonzalez, Slimane Thabet, Elham Kashefi, Jonas Landman. (2025). "Constrained and Vanishing Expressivity of Quantum Fourier Models." Quantum, vol.9, p.1847.
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