Abstract Bank is a business entity whose activities are collecting funds from the public in the form of deposits and channeling them to the public in the form of loans or other forms to improve the lives of many people. This study aims to classify the feasibility data of prospective borrowers with the perceptron algorithm using quantum computing to facilitate the bank in determining the prospective debtor. The results of this study are an analysis of the feasibility of prospective borrowers using the perceptron algorithm with quantum computing.
Abstract Bank is a business entity whose activities are collecting funds from the public in the form of deposits and channeling them to the public in the form of loans or other forms to improve the lives of many people. This study aims to classify the feasibility data of prospective borrowers with the perceptron algorithm using quantum computing to facilitate the bank in determining the prospective debtor. The results of this study are an analysis of the feasibility of prospective borrowers using the perceptron algorithm with quantum computing.
Quantum computing is a technology that takes advantage of quantum effects, which operates using quantum computing, resulting in increased algorithm speed compared to the speed of algorithms that use classical computing. This study aims to classify bank marketing data in determining the eligibility of customers who are entitled to become depositors with the quantum perceptron algorithm using calculations with 5 variables, namely employment, status, education, home credit, and bank loans. The result of this research is the analysis of bank marketing classification data to determine the eligibility of customers who are entitled to become depositors using the quantum perceptron algorithm with quantum computation.
The fundamental problem in this research is to explore a more profound understanding of 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 modeling. This research focuses on developing Madaline and Perceptron algorithms using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The data set used in this study is a lens data set. There are four attributes: 1) patient age: young, prepresbyopia, 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 that 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 the development of computing and neural networks, with potential applications extending from data processing to more accurate modeling in artificial intelligence, data analysis, and understanding complex patterns.
Quantum Perceptron is a combination method of artificial neural network and the concept of quantum computing. There are several proposed methods of quantum neural network where the methods have their own approach by using the concept of computing quantum. However, for several methods do not give detail exposure in deciding neural network architecture. Neural network architecture is most influential in the process of learning and testing in data classification. Therefore, presented some models of artificial neural network architecture using the concept of quantum computing is a superposition qubit. The qubit superposition is used to transform attribute values in the dataset so that the dataset can be used in the learning and testing process to obtain the value of the learning rate and the accuracy of the test. The dataset used is User Knowledge Modeling Dataset from the UCI Machine Learning Repository which contains students' knowledge about DC electric machine topic. The final result of this research shows that the value of learning rate and accuracy level of testing is very good in classifying the data.
Aim. IThe presented study aims to identify current trends and key directions for the introduction of quantum technologies into the management practices of Russian credit institutions to improve their performance in the medium term. Tasks. The authors comparatively analyze current trends in the application of quantum technologies in the banking business; forecast key trends in their development in the medium term in the context of the Russian banking sector. Methods. The authors use the methods of comparative and system analysis, scientific abstraction, and hypothesis. Results. Practical examples of the introduction of modern quantum technologies in the foreign and Russian banking business are provided. As a result, priority directions and areas of their application are identified with an emphasis on the domestic banking sector: credit scoring, assessment of the value and risk of investment, diversification of investment portfolios, financial forecasting, crisis prediction, fraud detection, protection of banking and customer databases from the quantum threat, and increasing the level of banking cybersecurity in general. Conclusions. Despite the novelty of quantum technologies in the banking business, the role they will play in banking in the near future is difficult to overestimate. Quantum computers can significantly improve the performance of banking IT systems by solving computational tasks that even super-powerful conventional computers are not able to tackle on their own. They significantly increase the efficiency of modeling complex systems and processes; accelerate the soultion of problems associated with resource optimization, logistics, finance, and trading; simplify work with big data using artificial intelligence (AI) and machine learning models in neural networks; help to combat cyber attacks and fraud in financial markets.
The creation of quantum circuit architecture based on the quantum perceptron algorithm to classify marketing bank data is proposed in this work. A quantum circuit is a quantum gate made up of two quantum gates. Quantum bits are used in this study's computation. The primary proposed learning method was not ideal, which is the context of this study. The percentage of qubits measurement value is still 90.7 percent. It is essential to raise the value of the qubit rate. Using the IBM Quantum Experience quantum computer, researchers measured, trained, and tested the quantum circuit architecture. Bank marketing data from the UCI Machine Learning Repository was used. A quantum circuit architecture model results from this research the quantum circuit measurement results.
Inside field of Machine Learning or ML one finds the formulation of artificial neural networks or ANN as a robust method to predict data under minimal bias. It constitutes a key procedure inside field of Artificial Intelligence that has demonstrated to be fast, efficient and autonomous in some cases. A deep understanding at the usage of ANN would lead to construct advanced as well as robust tools to predict behavior of systems. Mathematically speaking, once data has been trained, ML emerges as a powerful algorithm to predict unseen data but with a certain probability. Inspired in Quantum Mechanics being a theory fundamental in physic based at probabilities one can wonder if ANN based at the concept of perceptron might be improved through a mathematical methodology by using the formalism of Quantum Mechanics. The aim of paper is to explore novel mathematical mechanisms that would allow to fine the probabilities of prediction inside ML.