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This chapter concludes the tutorial by highlighting the potential of quantum machine learning (QML) to accelerate scientific discovery and real-world applications. It revisits the tutorial’s key themes, including quantum adaptations of classical models, theoretical insights, and implementation on near-term and future quantum devices. The chapter also emphasizes the importance of QML in domains such as drug discovery, material science, and optimization, where classical methods face scalability limits.

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Conclusion

  • 章节

  • 发表日期：

    2025年

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  • Yuxuan Du,
  • Xinbiao Wang,
  • Naixu Guo,
  • Zhan Yu,
  • Yang Qian,
  • Kaining Zhang,
  • Min-Hsiu Hsieh,
  • Patrick Rebentrost,
  • Dacheng Tao

摘要

This chapter concludes the tutorial by highlighting the potential of quantum machine learning (QML) to accelerate scientific discovery and real-world applications. It revisits the tutorial’s key themes, including quantum adaptations of classical models, theoretical insights, and implementation on near-term and future quantum devices. The chapter also emphasizes the importance of QML in domains such as drug discovery, material science, and optimization, where classical methods face scalability limits.