10 citations · 17 across the 3 of their papers we have counts for
7 papers · 1 filter
Machine learning applications for noisy intermediate-scale quantum computers
Brian Coyle
Quantum machine learning has proven to be a fruitful area in which to search for potential applications of quantum computers. This is particularly true for those available in the n…
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Chiara Leadbeater, Louis Sharrock, Brian Coyle +1
Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum c…
Variational inference with a quantum computer
Marcello Benedetti, Brian Coyle, Mattia Fiorentini +2
Inference is the task of drawing conclusions about unobserved variables given observations of related variables. Applications range from identifying diseases from symptoms to class…
Certified Randomness From Steering Using Sequential Measurements
Brian Coyle, Elham Kashefi, Matty Hoban
The generation of certifiable randomness is one of the most promising applications of quantum technologies. Furthermore, the intrinsic non-locality of quantum correlations allow us…
Quantum versus Classical Generative Modelling in Finance
Brian Coyle, Maxwell Henderson, Justin Chan Jin Le +3
Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impact…
Robust data encodings for quantum classifiers
Ryan LaRose, Brian Coyle
Data representation is crucial for the success of machine learning models. In the context of quantum machine learning with near-term quantum computers, equally important considerat…