Unitary quantum perceptron as efficient universal approximator (a) Articles uri icon

published in

  • EPL  Journal

publication date

  • March 2019

start page

  • 30004-p1

end page

  • 30004-p5

volume

  • 125

International Standard Serial Number (ISSN)

  • 0295-5075

Electronic International Standard Serial Number (EISSN)

  • 1286-4854

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.