迈向量子联合学习 量子联合学习
概括
量子联合学习 (QFL) 结合了量子计算和联合学习,以提高数据隐私和效率. 本综述提供了QFL原则,技术和应用的全面指南.
科学领域:
- 融合量子计算 (QC) 和联合学习 (FL) 的跨学科领域.
背景情况:
- 现有的研究缺乏对量子联合学习 (QFL) 的全面调查.
- QFL旨在通过量子技术提高机器学习的隐私,安全和效率.
研究的目的:
- 提供QFL的整体检查和全面理解.
- 确定QFL的挑战,机遇和未来的研究方向.
主要方法:
- 对量子联合学习当前研究的回顾.
- 基于特征和使用的量子方法,为QFL技术提出一个独特的分类法.
主要成果:
- 讨论QFL的原则,技术和新兴应用.
- 识别整合QC和FL的挑战和机会.
结论:
- QFL是一个快速发展的领域,有可能取得重大突破.
- 本综述是QFL研究人员和从业人员的基本指南.
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