Autonomous Quantum Perceptron Neural Network
This addresses the need for low-cost learning in applications requiring efficient computation, though it appears incremental as it builds on existing quantum neural network concepts.
The authors tackled the problem of high computational cost in classical neural networks by proposing a quantum perceptron neural network with a single neuron that uses self-adaptive activation operators, achieving learning in a limited number of iterations and reducing computational cost, with promising results demonstrated on various problems.
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.