Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model

Shiba, Kodai; Chen, Chih-Chieh; Sogabe, Masaru; Sakamoto, Katsuyoshi; Sogabe, Tomah

doi:10.3390/app112311386

Open AccessArticle

Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model

by

Kodai Shiba

^1,2,

Chih-Chieh Chen

²

,

Masaru Sogabe

²,

Katsuyoshi Sakamoto

^1,3 and

Tomah Sogabe

^1,2,3,*

¹

Engineering Department, The University of Electro-Communications, Tokyo 182-8585, Japan

²

Grid, Inc., Tokyo 107-0061, Japan

³

i-PERC, The University of Electro-Communications, Tokyo 182-8585, Japan

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2021, 11(23), 11386; https://doi.org/10.3390/app112311386

Submission received: 2 October 2021 / Revised: 2 November 2021 / Accepted: 9 November 2021 / Published: 1 December 2021

(This article belongs to the Topic Machine and Deep Learning)

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Abstract

Quantum computing is suggested as a new tool to deal with large data set for machine learning applications. However, many quantum algorithms are too expensive to fit into the small-scale quantum hardware available today and the loading of big classical data into small quantum memory is still an unsolved obstacle. These difficulties lead to the study of quantum-inspired techniques using classical computation. In this work, we propose a new classification method based on support vectors from a DBSCAN–Deutsch–Jozsa ranking and an Ising prediction model. The proposed algorithm has an advantage over standard classical SVM in the scaling with respect to the number of training data at the training phase. The method can be executed in a pure classical computer and can be accelerated in a hybrid quantum–classical computing environment. We demonstrate the applicability of the proposed algorithm with simulations and theory.

Keywords: quantum-inspired algorithm; Deutsch–Jozsa algorithm; Ising model; support-vector machine

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MDPI and ACS Style

Shiba, K.; Chen, C.-C.; Sogabe, M.; Sakamoto, K.; Sogabe, T. Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model. Appl. Sci. 2021, 11, 11386. https://doi.org/10.3390/app112311386

AMA Style

Shiba K, Chen C-C, Sogabe M, Sakamoto K, Sogabe T. Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model. Applied Sciences. 2021; 11(23):11386. https://doi.org/10.3390/app112311386

Chicago/Turabian Style

Shiba, Kodai, Chih-Chieh Chen, Masaru Sogabe, Katsuyoshi Sakamoto, and Tomah Sogabe. 2021. "Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model" Applied Sciences 11, no. 23: 11386. https://doi.org/10.3390/app112311386

APA Style

Shiba, K., Chen, C.-C., Sogabe, M., Sakamoto, K., & Sogabe, T. (2021). Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model. Applied Sciences, 11(23), 11386. https://doi.org/10.3390/app112311386

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Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model

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