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Quantum discriminator for binary classification.

Prasanna Date1, Wyatt Smith2

  • 1Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37830, USA. datepa@ornl.gov.

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|January 15, 2024
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Summary
This summary is machine-generated.

A new Quantum Discriminator model uses quantum computing's high-dimensional capabilities for machine learning. This quantum machine learning approach achieves 99% accuracy in simulations, offering a potential advantage over classical methods.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Classical computers face limitations in processing high-dimensional data.
  • Quantum computers offer unique capabilities for operating in high-dimensional spaces.
  • Machine learning models can benefit from enhanced computational power for complex tasks.

Purpose of the Study:

  • To introduce a novel quantum machine learning model, the Quantum Discriminator.
  • To leverage quantum computing's high-dimensional processing for improved machine learning.
  • To demonstrate the efficacy of the Quantum Discriminator on benchmark datasets.

Main Methods:

  • Developed a Quantum Discriminator model utilizing quantum-classical hybrid training.
  • Implemented inferencing on a universal quantum computer.
  • Input consists of binary features and a prediction qubit; output is the predicted label.

Main Results:

  • The Quantum Discriminator was trained in O(n) time and inferencing was performed in O(log n) time.
  • Achieved 99% accuracy in simulations on the Iris dataset.
  • Demonstrated high performance on the Bars and Stripes dataset.

Conclusions:

  • The Quantum Discriminator is a viable quantum machine learning model.
  • Quantum computing offers a significant advantage for machine learning tasks involving high-dimensional data.
  • The model shows potential for real-world applications requiring accurate classification.