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Quantum-Inspired Applications for Classification Problems.

Cesarino Bertini1, Roberto Leporini2

  • 1Department of Management, University of Bergamo, via dei Caniana 2, I-24127 Bergamo, Italy.

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This summary is machine-generated.

Quantum-inspired machine learning enhances classification using quantum state discrimination. A local k-nearest neighbors approach with quantum classifiers shows competitive performance on benchmark datasets.

Keywords:
classificationlocal approachquantum-inspired machine learning

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Data Science

Background:

  • Quantum state discrimination is a key tool in quantum information science.
  • Machine learning classification tasks can benefit from quantum-inspired algorithms.
  • Local approaches offer computational advantages in complex datasets.

Purpose of the Study:

  • To explore the efficacy of quantum state discrimination within a machine learning context.
  • To implement and evaluate a novel local approach combining k-nearest neighbors with quantum-inspired classifiers.
  • To benchmark the proposed method against established classification algorithms.

Main Methods:

  • Implementation of a local k-nearest neighbors (KNN) algorithm.
  • Integration of quantum-inspired classifiers within the KNN framework.
  • Performance evaluation using standard benchmark datasets.

Main Results:

  • The quantum-inspired local approach demonstrated competitive performance.
  • Comparison revealed comparable or superior results to well-known classical classifiers on specific datasets.
  • The study validates the utility of quantum state discrimination for classification.

Conclusions:

  • Quantum-inspired machine learning, particularly using quantum state discrimination, offers a promising avenue for classification.
  • The proposed local KNN with quantum classifiers provides an effective and efficient method.
  • Further research into quantum-inspired techniques for machine learning is warranted.