Publication:
Unitary quantum perceptron as efficient universal approximator (a)

Loading...
Thumbnail Image
Identifiers
Publication date
2019-03-04
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Science
Impact
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
Abstract
We demonstrate that it is possible to implement a quantum perceptron with a sigmoid activation function as an efficient, reversible many-body unitary operation. When inserted in a neural network, the perceptron's response is parameterized by the potential exerted by other neurons. We prove that such a quantum neural network is a universal approximator of continuous functions, with at least the same power as classical neural networks. While engineering general perceptrons is a challenging control problem —also defined in this work— the ubiquitous sigmoid-response neuron can be implemented as a quasi-adiabatic passage with an Ising model. In this construct, the scaling of resources is favorable with respect to the total network size and is dominated by the number of layers. We expect that our sigmoid perceptron will have applications also in quantum sensing or variational estimation of many-body Hamiltonians.
Description
Keywords
Bibliographic citation
Torrontegui, E. & García-Ripoll, J. J. (2019). Unitary quantum perceptron as efficient universal approximator. EPL (Europhysics Letters), 125(3), 30004.