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April 22, 2026Open Access

Quantum Deep Learning: A Comprehensive Review

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Authors

YJYanjun JiZCZhao-Yun ChenMRMarco Roth

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Overview

This review explores quantum deep learning paradigms and applications, highlighting implications for future implementations.

Key Points

  • The review aims to define quantum deep learning and categorize its main paradigms and applications.
  • Introduces a taxonomy of four paradigms in quantum deep learning.
  • Connects theoretical principles to practical implementations across various quantum computing systems.
  • Analyzes claims of quantum advantage and trade-offs in model performance and resource utilization.
  • Identifies four main paradigms of quantum deep learning: hybrid models, quantum neural networks, quantum algorithms, and quantum-inspired methods.
  • Discusses the critical assessment of quantum advantage and the constraints faced in practical applications.
  • Surveys applications in diverse fields such as image classification and natural language processing.

Cite This Study

Ji et al. (2026) studied this question.

synapsesocial.com/papers/69e867356e0dea528ddeb921https://doi.org/10.34734/fzj-2026-02245
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