Q-Madaline: Madaline Based On Qubit
Abstract
This research focuses on developing the MADALINE algorithm using quantum computing. Quantum computing uses binary numbers 0 or 1 or a combination of 0 and 1. The main problem in this research is how to find other alternatives to the MADALINE algorithm to solve pattern recognition problems with a quantum computing approach. The data used in this study are heart failure data to predict whether a patient is at risk of death. The data source comes from KAGGLE, consisting of 299 data with 12 symptoms and one target, alive or dead. The result of this study is an alternative to the MADALINE algorithm that uses quantum computing. The precision of the test results with MADALINE with a learning rate of 0.1 = 100% with 2 epochs. The accuracy of the test results using a quantum approach with a learning rate of 0.1 is 85.71%. The results of this study can be an alternative to the MADALINE algorithm with a quantum computing approach, although it has not shown better accuracy than the classical MADALINE algorithm. More research is needed to produce better accuracy with larger data.
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References
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, 2017.
D. Türkpençe, T. Ç. Akıncı, and Ş. Serhat, “Decoherence in a quantum neural network,” 2018.
I. Technology and U. S. Utara, “Using Quantum Circuits For Data Classification,” vol. 16, no. 11, pp. 1139–1146, 2022.
J. Allcock, C.-Y. Hsieh, I. Kerenidis, and S. Zhang, “Quantum algorithms for feedforward neural networks,” 2018.
N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum perceptron models,” Adv. Neural Inf. Process. Syst., no. Nips, pp. 4006–4014, 2016.
S. Ying, M. Ying, and Y. Feng, “Quantum Privacy-Preserving Perceptron,” 2017.
D. V. Fastovets, Y. I. Bogdanov, B. I. Bantysh, and V. F. Lukichev, “Machine learning methods in quantum computing theory,” p. 85, 2019.
P. Auer, H. Burgsteiner, and W. Maass, “Reducing communication for distributed learning in neural networks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 2415, pp. 123–128, 2002.
X. Hu, A. Tan, and Y. Gao, “The Construction of the Development Mode of School-Enterprise Cooperation in Higher Vocational Education with the Aid of Sensitive Neural Network,” Wirel. Commun. Mob. Comput., vol. 2022, 2022.
F. A. Setyaningsih, “Interval Regression With Neuro-Fuzzy and Madaline Architecture for Prediction of Rice Production,” vol. 102, no. Ictvt, pp. 35–40, 2017.
S. Saha, R. Lahiri, A. Konar, and A. K. Nagar, “A novel approach to American sign language recognition using MAdaline neural network,” 2016 IEEE Symp. Ser. Comput. Intell. SSCI 2016, 2017.
S. Zhong et al., “A sensitivity-based improving learning algorithm for madaline rule II,” Math. Probl. Eng., vol. 2014, 2014.
K. L. Ambashtha, ““ A Study On Indian Stock Market Prediction Using Statistical Tools “,” vol. 6, no. 6, pp. 759–773, 2019.
D. D. G. Ramírez, E. L. Giraldo, J. Diego, and J. Vargas, “Acercamiento a redes neuronales artificiales Madaline y Base Radial aplicándolas a un decodificador binario de 9 bits.”
R. Edition and E. Walach, “Appendix G: Thirty Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation,” Adapt. Inverse Control, vol. 78, no. 9, pp. 409–474, 2007.
R. Winter and B. Widrow, “Madaline rule II: A training algorithm for neural networks,” Neural Networks, vol. 1, no. 1 SUPPL, p. 148, 1988.
S. Al-mashahadany, “Neural Network with Madaline for Machine Printed English Character Recognition,” AL-Rafidain J. Comput. Sci. Math., vol. 8, no. 1, pp. 47–58, 2011.
M. Sahami, “Learning non-linearly separable boolean functions with linear threshold unit trees and Madaline-style networks,” Proc. Natl. Conf. Artif. Intell., pp. 335–341, 1993.
B. Widrow and M. A. Lehr, “30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation,” Proc. IEEE, vol. 78, no. 9, pp. 1415–1442, 1990.
Solikhun, M. Wahyudi, M. Safii, and M. Zarlis, “Backpropagation Network Optimization Using One Step Secant (OSS) Algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 769, no. 1, 2020.
K. Chiewchanchairat, P. Bumroongsri, and S. Kheawhom, “KKU Engineering Journal,” KKU Eng. J., vol. 40, no. March, pp. 131–138, 2013.
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