A Comparison of Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach

Abstract

The fundamental problem in this research is to explore a more profound understanding regarding both performance and efficiency in quantity computing. Successful implementation of algorithms in computational computing environments can unlock the potential for significant improvements in information processing and neural network modelling. This research focuses on developing the Madaline and Perceptron algorithm using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The dataset used in this study is using a lens dataset. There are four attributes: 1) patient age: young, pre-presbyopia, presbyopia 2) eyeglass prescription: myopia, hypermetropia 3) astigmatic: no, yes. 4) tear production rate: reduced, normal. There are three classes: 1) patients must have hard contact lenses installed, 2) patients must have soft contact lenses installed, and 3) patients cannot have contact lenses installed. The number of data is 24 data. The result of this research is the development of the Madaline and Perceptron algorithms with a quantum computing approach. Comparing these algorithms shows the best accuracy is the Perceptron algorithm, namely 100%. In comparison, Madaline is 62.5%, and the smallest epoch is the Madaline algorithm, namely 4 epochs, while the smallest Perceptron epoch is 317. This research significantly contributes to developing computing and neural networks, with potential applications extending from data processing to more accurate modelling in artificial intelligence, data analysis and understanding complex patterns.