6 citations · 10 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
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 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…
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…