Related Experiment Video
Updated: May 12, 2025

05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
457
Toward Quantum Federated Learning
Summary
Quantum federated learning (QFL) combines quantum computing and federated learning to enhance data privacy and efficiency. This review provides a comprehensive guide to QFL principles, techniques, and applications.
Area of Science:
- Interdisciplinary field merging quantum computing (QC) and federated learning (FL).
Background:
- Existing research lacks a comprehensive survey of quantum federated learning (QFL).
- QFL aims to enhance privacy, security, and efficiency in machine learning using quantum technologies.
Purpose of the Study:
- To provide a holistic examination and comprehensive understanding of QFL.
- To identify challenges, opportunities, and future research directions in QFL.
Main Methods:
- Review of current research in quantum federated learning.
- Proposal of a unique taxonomy for QFL techniques based on characteristics and quantum methods employed.
Main Results:
- Discussion of the principles, techniques, and emerging applications of QFL.
- Identification of challenges and opportunities in integrating QC and FL.
Conclusions:
- QFL is a rapidly evolving field with potential for significant breakthroughs.
- This review serves as a foundational guide for researchers and practitioners in QFL.
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