Variational Quantum Support Vector Machine based on Deutsch-Jozsa Ranking

Koudai Shiba, Katsuyoshi Sakamoto, Tomah Sogabe


Recently, machine learning algorithms using quantum computers have been actively developed. Among them, the support vector machine (SVM) specialized for the classification problem is one of the algorithms that are attracting the most attention for improving the performance by the quantum computer. In this study, we propose an SVM that classifies test data according to energy level by using time evolution calculation of Ising model. Furthermore, we propose a method to determine the support vector by applying the concept of DBSCAN and Deutsch-Jozsa algorithm and examining the mixture of surrounding data classes. We show that our algorithm can be classified regardless of the difference in the dimensions of the teacher data, and also the difference between linear and nonlinear data.


Deutsch-Jozsa algorithm; machine learning; support vector machine; quantum algorithms

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