Variational quantum approximate support vector machine with inference transfer

被引：14

作者：

Park, Siheon ^{[1
]}

Park, Daniel K. K. ^{[2
,3
]}

Rhee, June-Koo Kevin ^{[1
,4
]}

机构：

[1] Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon 34141, South Korea

[2] Yonsei Univ, Dept Appl Stat, Seoul 03722, South Korea

[3] Yonsei Univ, Dept Stat & Data Sci, Seoul 03722, South Korea

[4] Qunova Comp Inc, Daejeon 34051, South Korea

来源：

SCIENTIFIC REPORTS | 2023年 / 13卷 / 01期

关键词：

D O I：

10.1038/s41598-023-29495-y

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.

引用

页数：10

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