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Optimizing Variational Quantum Neural Networks Based on Collective Intelligence

Author

Listed:
  • Zitong Li

    (School of Information, Hunan University of Humanities, Science and Technology, Loudi 417000, China
    These authors contributed equally to this work.)

  • Tailong Xiao

    (State Key Laboratory of Advanced Optical Communication Systems and Networks, Institute of Quantum Sensing and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
    These authors contributed equally to this work.)

  • Xiaoyang Deng

    (Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Guihua Zeng

    (State Key Laboratory of Advanced Optical Communication Systems and Networks, Institute of Quantum Sensing and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Weimin Li

    (School of Information, Hunan University of Humanities, Science and Technology, Loudi 417000, China)

Abstract

Quantum machine learning stands out as one of the most promising applications of quantum computing, widely believed to possess potential quantum advantages. In the era of noisy intermediate-scale quantum, the scale and quality of quantum computers are limited, and quantum algorithms based on fault-tolerant quantum computing paradigms cannot be experimentally verified in the short term. The variational quantum algorithm design paradigm can better adapt to the practical characteristics of noisy quantum hardware and is currently one of the most promising solutions. However, variational quantum algorithms, due to their highly entangled nature, encounter the phenomenon known as the “barren plateau” during the optimization and training processes, making effective optimization challenging. This paper addresses this challenging issue by researching a variational quantum neural network optimization method based on collective intelligence algorithms. The aim is to overcome optimization difficulties encountered by traditional methods such as gradient descent. We study two typical applications of using quantum neural networks: random 2D Hamiltonian ground state solving and quantum phase recognition. We find that the collective intelligence algorithm shows a better optimization compared to gradient descent. The solution accuracy of ground energy and phase classification is enhanced, and the optimization iterations are also reduced. We highlight that the collective intelligence algorithm has great potential in tackling the optimization of variational quantum algorithms.

Suggested Citation

  • Zitong Li & Tailong Xiao & Xiaoyang Deng & Guihua Zeng & Weimin Li, 2024. "Optimizing Variational Quantum Neural Networks Based on Collective Intelligence," Mathematics, MDPI, vol. 12(11), pages 1-14, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1627-:d:1399785
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    References listed on IDEAS

    as
    1. 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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