4 papers · 1 filter
Spectral Born machines: classically trainable quantum generative models for discrete data
Austin Huang, William Maxwell, Vasilis Belis +4
We present \emph{spectral Born machines}, a class of quantum generative models that results from viewing and generalizing the class of IQP Born machines through the lens of group F…
Spectral methods: crucial for machine learning, natural for quantum computers?
Vasilis Belis, Joseph Bowles, Rishabh Gupta +2
This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularis…
Probabilistic modeling over permutations using quantum computers
Vasilis Belis, Giulio Crognaletti, Matteo Argenton +2
Quantum computers provide a super-exponential speedup for performing a Fourier transform over the symmetric group, an ability for which practical use cases have remained elusive so…
Learning Reduced Representations for Quantum Classifiers
Patrick Odagiu, Vasilis Belis, Lennart Schulze +6
Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this im…