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International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)

New Architecture Quantum Perceptron using Quantum Circuit

Solikhun Solikhun, Syahril Efendi, Muhammad Zarlis, Poltak Sihombing

Published Nov 11, 2021• 1 citation
Publisher

Abstract

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%.

DOI
10.1109/databia53375.2021.9650098

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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.
This paper highlights a practical research of the possibility of forming quantum circuits for training neural networks. The demonstrated quantum circuits were based on the principles of Grover's Search Algorithm. The perceptron was chosen as the architecture for the example neural network. The multilayer perceptron is a popular neural network architecture due to its scalability and applicability for solving a wide range of problems. Large scale neural networks can require significant computing resources for training purposes, therefore Grover’s quantum search algorithm could be used as a way to speed up this process, thanks to its ability of simultaneously processing multiple input values of a given function.
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.