2.5k citations · 12.5k across the 36 of their papers we have counts for
39 papers
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…
Group Fourier filtering of quantum resources in quantum phase space
Luke Coffman, N. L. Diaz, Martin Larocca +2
Recently, it has been shown that group Fourier analysis of quantum states, i.e., decomposing them into the irreducible representations (irreps) of a symmetry group, enables new way…
Quantum Machine Learning
Muhammad Usman
The meteoric rise of artificial intelligence in recent years has seen machine learning methods become ubiquitous in modern science, technology, and industry. Concurrently, the emer…
Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data
David Wakeham, Maria Schuld
How can we take inspiration from a typical quantum algorithm to design heuristics for machine learning? A common blueprint, used from Deutsch-Josza to Shor's algorithm, is to place…
Better than classical? The subtle art of benchmarking quantum machine learning models
Joseph Bowles, Shahnawaz Ahmed, Maria Schuld
Benchmarking models via classical simulations is one of the main ways to judge ideas in quantum machine learning before noise-free hardware is available. However, the huge impact o…