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A Modified Depolarization Approach for Efficient Quantum Machine Learning

Author

Listed:
  • Bikram Khanal

    (School of Engineering and Computer Science, Department of Computer Science, Baylor University, Waco, TX 76798, USA)

  • Pablo Rivas

    (School of Engineering and Computer Science, Department of Computer Science, Baylor University, Waco, TX 76798, USA)

Abstract

Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite these progresses, challenges persist due to system noise, errors, and decoherence. These system noises complicate the simulation of quantum systems. The depolarization channel is a standard tool for simulating a quantum system’s noise. However, modeling such noise for practical applications is computationally expensive when we have limited hardware resources, as is the case in the NISQ era. This work proposes a modified representation for a single-qubit depolarization channel. Our modified channel uses two Kraus operators based only on X and Z Pauli matrices. Our approach reduces the computational complexity from six to four matrix multiplications per channel execution. Experiments on a Quantum Machine Learning (QML) model on the Iris dataset across various circuit depths and depolarization rates validate that our approach maintains the model’s accuracy while improving efficiency. This simplified noise model enables more scalable simulations of quantum circuits under depolarization, advancing capabilities in the NISQ era.

Suggested Citation

  • Bikram Khanal & Pablo Rivas, 2024. "A Modified Depolarization Approach for Efficient Quantum Machine Learning," Mathematics, MDPI, vol. 12(9), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1385-:d:1387442
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    References listed on IDEAS

    as
    1. Aram W. Harrow & Ashley Montanaro, 2017. "Quantum computational supremacy," Nature, Nature, vol. 549(7671), pages 203-209, September.
    2. Vojtěch Havlíček & Antonio D. Córcoles & Kristan Temme & Aram W. Harrow & Abhinav Kandala & Jerry M. Chow & Jay M. Gambetta, 2019. "Supervised learning with quantum-enhanced feature spaces," Nature, Nature, vol. 567(7747), pages 209-212, March.
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