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The fusion between supervised learning and quantum computing promises substantial improvements in comparison with classic algorithms in the NISQ era, fuelling great interest for further developments in industry and academia. This chapter provides a review of the current state of quantum machine learning in supervised tasks, focusing on papers published in English in scientific journals or presented at conferences from January 2018 to June 2024, sourced from the SCOPUS database. Various papers were selected according to the PRISMA methodology that focus into general aspects of quantum supervised machine learning for variational algorithms and oracles, including theoretical case studies as well as examples and performance comparisons with classical versions on various datasets.

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Supervised Quantum Machine Learning

  • 章节

  • 发表日期：

    2025年

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  • A. Monroy-Azpeitia,
  • J. Y. Montiel-Pérez,
  • A. Maldonado-Romo,
  • A. A. Arvizu-Velazquez,
  • J. L. López-Bonilla,
  • S. Vidal-Beltrán

摘要

The fusion between supervised learning and quantum computing promises substantial improvements in comparison with classic algorithms in the NISQ era, fuelling great interest for further developments in industry and academia. This chapter provides a review of the current state of quantum machine learning in supervised tasks, focusing on papers published in English in scientific journals or presented at conferences from January 2018 to June 2024, sourced from the SCOPUS database. Various papers were selected according to the PRISMA methodology that focus into general aspects of quantum supervised machine learning for variational algorithms and oracles, including theoretical case studies as well as examples and performance comparisons with classical versions on various datasets.