10 citations · 10 across the 1 of their papers we have counts for
8 papers
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…
Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack +1
Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regard…
Structure optimization for parameterized quantum circuits
Mateusz Ostaszewski, Edward Grant, Marcello Benedetti
We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits…
An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski +1
Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of…
Training of Quantum Circuits on a Hybrid Quantum Computer
D. Zhu, N. M. Linke, M. Benedetti +10
Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to tak…