Efficient Discrete Feature Encoding for Variational Quantum Classifier

Hiroshi Yano, Yudai Suzuki, Rudy Raymond, Naoki Yamamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving machine learning tasks in, e.g., classification, regression, and recommender systems. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational Quantum Classification (VQC) is one of such variational methods with possible quantum advantage in using quantum-enhanced features that are hard to compute by classical methods. Its performance depends on the mapping of classical features into quantum-enhanced feature space. Although there have been many quantum-mapping functions proposed so far, there is little discussion on efficient mapping of discrete features, such as, race, gender, marriage status and others that are often significant for classifying datasets of interest. We first introduce the use of Quantum Random Access Coding (QRAC) to map such discrete features efficiently into limited number of qubits for VQC. We numerically show that QRAC can help speeding up the training of VQC by reducing its parameters via reduction on the number of qubits for the mapping. We confirm the effectiveness of the QRAC in VQC by experimenting on classification of healthcare datasets with both simulators and real quantum devices.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020
EditorsHausi A. Muller, Greg Byrd, Candace Culhane, Erik DeBenedictis, Travis Humble
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11-21
Number of pages11
ISBN (Electronic)9781728189697
DOIs
Publication statusPublished - 2020 Oct
Event2020 IEEE International Conference on Quantum Computing and Engineering, QCE 2020 - Denver, United States
Duration: 2020 Oct 122020 Oct 16

Publication series

NameProceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020

Conference

Conference2020 IEEE International Conference on Quantum Computing and Engineering, QCE 2020
Country/TerritoryUnited States
CityDenver
Period20/10/1220/10/16

Keywords

  • discrete features
  • quantum machine learning
  • quantum random access coding
  • supervised learning
  • variational quantum algorithms

ASJC Scopus subject areas

  • Modelling and Simulation
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Computer Science Applications
  • Hardware and Architecture

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