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Dissipative Realization of a Quantum Distance-Based Classifier Using Open Quantum Walks.

Pedro Linck Maciel1, Graeme Pleasance2, Francesco Petruccione2,3

  • 1Departamento de Física, Centro de Ciências Exatas e da Natureza, Universidade Federal de Pernambuco, Recife 50670-901, Brazil.

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Summary
This summary is machine-generated.

Open quantum walks (OQWs) enable dissipative quantum computation. This study shows a quantum classifier runs efficiently within the OQW model, maintaining finite runtime even in slower environments.

Keywords:
Hadamard classifierdissipative quantum computationdistance-based classifieropen quantum systemsopen quantum walksquantum distance-based classifierquantum machine learning

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Area of Science:

  • Quantum Computing
  • Quantum Information Science
  • Environmental Interactions in Quantum Systems

Background:

  • Open quantum walks (OQWs) are driven by environmental interactions.
  • OQWs offer a framework for dissipative quantum computation.

Purpose of the Study:

  • To demonstrate the feasibility of a quantum distance-based classifier using the OQW model.
  • To analyze the runtime of the classifier within the OQW framework.

Main Methods:

  • Implementation of a quantum distance-based classifier.
  • Utilizing the open quantum walk computation model.
  • Analysis of expected runtime in varying regimes.

Main Results:

  • Successful demonstration of the quantum classifier within the OQW model.
  • Expected runtime remains finite, even in slower OQW dynamics.

Conclusions:

  • The OQW model is a viable platform for implementing quantum classifiers.
  • The proposed classifier exhibits robust performance with finite expected runtime.