108 citations · 539 across the 15 of their papers we have counts for
15 papers
When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square
Slimane Thabet, Léo Monbroussou, Eliott Z. Mamon +1
Quantum Machine Learning algorithms based on Variational Quantum Circuits (VQCs) are important candidates for useful application of quantum computing. It is known that a VQC is a l…
Subspace Preserving Quantum Convolutional Neural Network Architectures
Léo Monbroussou, Jonas Landman, Letao Wang +2
Subspace preserving quantum circuits are a class of quantum algorithms that, relying on some symmetries in the computation, can offer theoretical guarantees for their training. Tho…
Constrained and Vanishing Expressivity of Quantum Fourier Models
Hela Mhiri, Leo Monbroussou, Mario Herrero-Gonzalez +3
In this work, we highlight an unforeseen behavior of the expressivity of Parameterized Quantum Circuits (PQCs) for machine learning. A large class of these models, seen as Fourier…
Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
Léo Monbroussou, Eliott Z. Mamon, Jonas Landman +3
Quantum machine learning (QML) has become a promising area for real world applications of quantum computers, but near-term methods and their scalability are still important researc…
Quantum Fourier Networks for Solving Parametric PDEs
Nishant Jain, Jonas Landman, Natansh Mathur +1
Many real-world problems, like modelling environment dynamics, physical processes, time series etc., involve solving Partial Differential Equations (PDEs) parameterised by problem-…
Quantum Methods for Neural Networks and Application to Medical Image Classification
Jonas Landman, Natansh Mathur, Yun Yvonna Li +4
Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications. In this paper, we introduce two new quantum met…
Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
Jonas Landman, Slimane Thabet, Constantin Dalyac +2
Many applications of quantum computing in the near term rely on variational quantum circuits (VQCs). They have been showcased as a promising model for reaching a quantum advantage…
Quantum Vision Transformers
El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur +3
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very perform…
Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
Jonas Landman
In this thesis, we investigate whether quantum algorithms can be used in the field of machine learning for both long and near term quantum computers. We will first recall the funda…
Medical image classification via quantum neural networks
Natansh Mathur, Jonas Landman, Yun Yvonna Li +4
Machine Learning provides powerful tools for a variety of applications, including disease diagnosis through medical image classification. In recent years, quantum machine learning…
Quantum Bayesian Neural Networks
Noah Berner, Vincent Fortuin, Jonas Landman
Quantum machine learning promises great speedups over classical algorithms, but it often requires repeated computations to achieve a desired level of accuracy for its point estimat…
Classical and Quantum Algorithms for Orthogonal Neural Networks
Iordanis Kerenidis, Jonas Landman, Natansh Mathur
Orthogonal neural networks have recently been introduced as a new type of neural networks imposing orthogonality on the weight matrices. They could achieve higher accuracy and avoi…
Quantum Spectral Clustering
Iordanis Kerenidis, Jonas Landman
Spectral clustering is a powerful unsupervised machine learning algorithm for clustering data with non convex or nested structures. With roots in graph theory, it uses the spectral…
Quantum Algorithms for Deep Convolutional Neural Networks
Iordanis Kerenidis, Jonas Landman, Anupam Prakash
Quantum computing is a new computational paradigm that promises applications in several fields, including machine learning. In the last decade, deep learning, and in particular Con…
q-means: A quantum algorithm for unsupervised machine learning
Iordanis Kerenidis, Jonas Landman, Alessandro Luongo +1
Quantum machine learning is one of the most promising applications of a full-scale quantum computer. Over the past few years, many quantum machine learning algorithms have been pro…