Autonomous Quantum Perceptron Neural Network

Abstract

Recently, with the rapid development of technology, there are a lot of applications require to achieve low-cost learning. However the computational power of classical artificial neural networks, they are not capable to provide low-cost learning. In contrast, quantum neural networks may be representing a good computational alternate to classical neural network approaches, based on the computational power of quantum bit (qubit) over the classical bit. In this paper we present a new computational approach to the quantum perceptron neural network can achieve learning in low-cost computation. The proposed approach has only one neuron can construct self-adaptive activation operators capable to accomplish the learning process in a limited number of iterations and, thereby, reduce the overall computational cost. The proposed approach is capable to construct its own set of activation operators to be applied widely in both quantum and classical applications to overcome the linearity limitation of classical perceptron. The computational power of the proposed approach is illustrated via solving variety of problems where promising and comparable results are given.

11 pages, 2 figures

Cited by in corpus (8)

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• Simulating a perceptron on a quantum computer
• Quantum perceptron over a field and neural network architecture selection in a quantum computer
• Advances in quantum machine learning
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• Opening the black box of deep learning
• Simulation of a Variational Quantum Perceptron using Grover's Algorithm
• Efficient learning algorithm for quantum perceptron unitary weights