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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

Development of Quantum Circuit Architecture on Quantum Perceptron Algorithm for Classification of Marketing Bank Data

Mochamad Wahyudi, Solikhun Solikhun

Published Feb 1, 2023• 0 citations
Publisher

Abstract

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.

DOI
10.29207/resti.v7i1.4526

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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.
the learning algorithm that is not optimal is due to the incomplete use of quantum in the perceptron quantum learning algorithm which is the background of this research. Previous research has shown that the proposed architectural form still needs to increase its probability value to be more optimal. The previous probability was 90.7%. The proposed model is a quantum circuit architecture in a quantum perceptron algorithm consisting of a quantum bit gate. The researchers conducted training and testing of the proposed quantum circuit architecture using quantum computer of the IBM Quantum Experience. This research produces a perceptron quantum architecture model using a quantum circuit that can solve problems regarding data classification. After measuring the proposed quantum circuit architecture, it shows a 100% probability. After training and testing, the probability is 100%, the same as that at the time of measurement. Then it produces the same output of |111100000000000000〉. The resulting probability shows a significant increase from the previous 90, 7%.
Abstract This research is motivated by the incomplete use of quantum in existing learning algorithms, so that the proposed learning algorithm is not optimal. Research (Fahri & Neven, 2018) shows that the proposed method of architectural form still uses classical architecture but inputs, weights and targets already use a quantum approach. Based on the results of previous studies, it shows that quantum computing is better than classical computation. Many researchers use quantum computing in the proposed learning algorithm. The model proposed is a quantum circuit architecture with the quantum perceptron method consisting of a quantum bit gate that uses a quantum computational approach as the architecture of the quantum perceptron learning algorithm. Then the authors conduct training and testing of the proposed quantum circuit architecture to test the quantum circuit model that the author proposes. The result of this research is a quantum circuit model with the quantum perceptron method which can be used to solve the learning optimization problem by using a quantum circuit architecture with 5 measurement measurements to show error training and testing = 0, with 9 measurements showing an error training of 1.13%, error testing 2.06%.
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.
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.
Perceptron model is a fundamental linear classifier in machine learning and also the building block of artificial neural networks. Recently, Wiebe et al. (arXiv:1602.04799) proposed that the training of a perceptron can be quadratically speeded using Grover search with a quantum computer, which has potentially important big-data applications. In this paper, we design a quantum circuit for implementing this algorithm. The Grover oracle, the central part of the circuit, is realized by Quantum-Fourier-Transform based arithmetics that specifies whether an input weight vector can correctly classify all training data samples. We also analyze the required number of qubits and universal gates for the algorithm, as well as the success probability using uniform sampling, showing that it has higher possibility than spherical Gaussian distribution N(0,1)N(0,1)N(0,1). The feasibility of the circuit is demonstrated by a testing example using the IBM-Q cloud quantum computer, where 16 qubits are used to classify four data samples.
Quantum Computer is a computer machine using principles of quantum mechanics in doing its computation. The Quantum Computer Machine itself is still in the development stage and has not been deployed yet, however TensorFlow provides a library for hybrid quantum-classical machine learning called TensorFlow Quantum (TFQ). One of the quantum computing models is the Quantum Neural Network (QNN). QNN is adapted from classical neural networks capable of processing qubit data and passing quantum circuits. QNN is a machine learning model that allows quantum computers to classify image data. The image data used is classical data, but classical data cannot reach a superposition state. So in order to carry out this protocol, the data must be readable into a quantum device that provides superposition. QNN uses a supervised learning method to predict image data. Quantum Neural Network (QNN) with a supervised learning method for classifying handwritten numeric image data is implemented using a quantum computer circuit simulation using the Python 3.6 programming language. The quantum computer circuit simulation is designed using library Cirq and TFQ. The classification process is carried out on Google Colab. The results of training on the QNN model obtained a value of 0.337 for the loss value and 0.3427 for the validation loss value. Meanwhile, the hinge accuracy value from the training results is 0.8603 for the hinge accuracy value with training data and 0.8669 for the hinge accuracy validation value. Model testing is done by providing 100 handwritten number images that are tested, with 53 image data of number three and 47 image data of number six. The results obtained for the percentage of testing accuracy are 100% for the number three image and 100% for the number six image. Thus, the total percentage of testing is 100%.