Quantum Discriminant Analysis for Dimensionality Reduction and Classification

Abstract

We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set. Compared with the best-known classical algorithms, the quantum algorithms show an exponential speedup in both the number of training vectors M and the feature space dimension N.

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Document Details

Document Type
Technical Report
Publication Date
Jul 06, 2016
Accession Number
AD1053571

Entities

People

  • Iris Cong
  • Luming Duan

Organizations

  • University of California

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Alzheimer Disease
  • Big Data
  • Cirrhosis
  • Computer Science
  • Data Analysis
  • Data Mining
  • Data Science
  • Dimensionality Reduction
  • Discriminant Analysis
  • Information Science
  • Machine Learning
  • Probability
  • Quantum Computing
  • Quantum Information
  • Statistical Analysis
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Operations Research
  • Quantum Chemistry
  • Regression Analysis.

Technology Areas

  • Quantum Computing
  • Space
  • Space - Space Objects