Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.

翻译：量子联邦学习（QFL）是一个新兴的跨学科领域，它融合了量子计算（QC）与联邦学习（FL）的原理，旨在利用量子技术增强学习过程中的隐私保护、安全性和效率。目前，该跨学科领域尚无全面的综述。本文对QFL进行了深入且整体的审视，旨在提供对QFL原理、技术及新兴应用的全面理解。我们探讨了这一快速发展领域的研究现状，指出了整合这些技术过程中的挑战与机遇，并概述了未来方向及待解决的开放性问题。我们提出了一种独特的QFL技术分类法，根据其特性及所使用的量子技术进行分类。随着QFL领域的持续发展，我们可以期待在各行各业中取得进一步突破和应用，推动创新，并应对数据隐私、安全性和资源优化等方面的挑战。本综述作为首份全面指南，为有兴趣理解和推进QFL领域的研究人员和从业者提供了重要参考。