Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks

Abstract

Quantum computing enables quantum neural networks (QNNs) to have great potentials to surpass artificial neural networks (ANNs). The powerful generalization of neural networks is attributed to nonlinear activation functions. Although various models related to QNNs have been developed, they are facing the challenge of merging the nonlinear, dissipative dynamics of neural computing into the linear, unitary quantum system. In this paper, we establish different quantum circuits to approximate nonlinear functions and then propose a generalizable framework to realize any nonlinear quantum neuron. We present two quantum neuron examples based on the proposed framework. The quantum resources required to construct a single quantum neuron are the polynomial, in function of the input size. Finally, both IBM Quantum Experience results and numerical simulations illustrate the effectiveness of the proposed framework.

9 pages, 8 figures

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• Parametrized constant-depth quantum neuron
• Unitary Evolutions Sourced By Interacting Quantum Memories: Closed Quantum Systems Directing Themselves Using Their State Histories
• An Amplitude-Based Implementation of the Unit Step Function on a Quantum Computer
• Dissipative learning of a quantum classifier
• A Quantum Convolutional Neural Network for Image Classification