11 citations · 15 across the 2 of their papers we have counts for
8 papers
Learnability of the output distributions of local quantum circuits
Marcel Hinsche, Marios Ioannou, Alexander Nietner +6
There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two differe…
The effect of data encoding on the expressive power of variational quantum machine learning models
Maria Schuld, Ryan Sweke, Johannes Jakob Meyer
Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs to predictions. While a lot of work has been done to…
On the Quantum versus Classical Learnability of Discrete Distributions
Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter +1
Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More specifically we consi…
Tensor network approaches for learning non-linear dynamical laws
A. Goeßmann, M. Götte, I. Roth +3
Given observations of a physical system, identifying the underlying non-linear governing equation is a fundamental task, necessary both for gaining understanding and generating det…
Stochastic gradient descent for hybrid quantum-classical optimization
Ryan Sweke, Frederik Wilde, Johannes Meyer +4
Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of…
Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning
Ivan Glasser, Ryan Sweke, Nicola Pancotti +2
Tensor-network techniques have enjoyed outstanding success in physics, and have recently attracted attention in machine learning, both as a tool for the formulation of new learning…