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Variational quantum approximate support vector machine with inference transfer.

Siheon Park1, Daniel K Park2,3, June-Koo Kevin Rhee4,5

  • 1KAIST, School of Electrical Engineering, Daejeon, 34141, South Korea.

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Summary
This summary is machine-generated.

We introduce a Variational Quantum Approximate Support Vector Machine (VQASVM) for efficient data classification. This quantum machine learning algorithm shows promise for complex datasets on current quantum hardware.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Data Classification

Background:

  • Kernel-based quantum classifiers are vital for hyper-linear classification of complex data.
  • Quantum machine learning (QML) offers advanced computational capabilities.

Purpose of the Study:

  • To propose a novel Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm.
  • To demonstrate its feasibility on current Noisy Intermediate-Scale Quantum (NISQ) computers.
  • To assess the algorithm's practicality and scalability for real-world datasets.

Main Methods:

  • Developed a VQASVM algorithm leveraging quantum operations.
  • Conducted proof-of-concept experiments on a toy dataset using cloud-based NISQ machines.
  • Performed numerical investigations on the Iris flower and MNIST datasets.

Main Results:

  • The VQASVM algorithm exhibits empirical sub-quadratic run-time complexity.
  • Quantum operations are compatible with NISQ hardware limitations.
  • Successful classification demonstrated on benchmark datasets, confirming practicality and scalability.

Conclusions:

  • The VQASVM algorithm presents a practical and efficient approach to quantum-enhanced data classification.
  • It offers a viable quantum machine learning solution for complex datasets on existing quantum hardware.
  • Further research can explore its application in diverse scientific and industrial domains.