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An optimizing method for performance and resource utilization in quantum machine learning circuits.

Tahereh Salehi1, Mariam Zomorodi2,3, Pawel Plawiak4,5

  • 1Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

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|October 10, 2022
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Summary
This summary is machine-generated.

This study introduces an optimization method for quantum machine learning circuits, significantly reducing quantum resource costs and improving performance for big data applications. The approach optimizes quantum circuits, leading to faster execution and lower resource requirements.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Computational Science

Background:

  • Quantum computing leverages quantum mechanics for enhanced computational power.
  • Quantum machine learning offers potential for exponential speedups over classical algorithms.
  • High quantum resource costs and environmental interaction challenges hinder current quantum machine learning implementations, especially for big data.

Purpose of the Study:

  • To propose and evaluate an approach for optimizing quantum machine learning circuits.
  • To reduce the cost and improve the performance of quantum machine learning algorithms for big data.
  • To decrease the number of quantum gates and computational time while preserving functionality.

Main Methods:

  • Development of an optimization approach utilizing various optimization algorithms.
  • Application of the optimization method to quantum machine learning algorithms designed for big data.
  • Analysis of circuit gate reduction and performance improvements.

Main Results:

  • The proposed approach optimizes quantum machine learning circuits, reducing quantum gate count by 10.7% and 14.9% in specific sub-circuits.
  • Optimization rates increase when sub-circuits are repeated, leading to greater overall efficiency.
  • Achieved reduction in quantum gates and improved execution time for quantum machine learning algorithms on big data.

Conclusions:

  • The developed method effectively reduces the cost and enhances the performance of quantum machine learning circuits for big data.
  • Optimized quantum circuits offer a more efficient pathway for implementing complex machine learning tasks using quantum computation.
  • This work addresses key challenges in resource utilization and computational efficiency for practical quantum machine learning applications.