11 citations · 15 across the 2 of their papers we have counts for
6 papers · 1 filter
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…
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…
Lieb-Robinson bounds for open quantum systems with long-ranged interactions
Ryan Sweke, Jens Eisert, Michael Kastner
We state and prove four types of Lieb-Robinson bounds valid for many-body open quantum systems with power law decaying interactions undergoing out of equilibrium dynamics. We also…
Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
Ryan Sweke, Markus S. Kesselring, Evert P. L. van Nieuwenburg +1
Topological error correcting codes, and particularly the surface code, currently provide the most feasible roadmap towards large-scale fault-tolerant quantum computation. As such,…