QUANTUM FEDERATED LEARNING WITH QUANTUM DATA

被引:33
作者
Chehimi, Mahdi [1 ]
Saad, Walid [1 ]
机构
[1] Virginia Tech, Bradley Dept Elect & Comp Engn, Wireless VT, Blacksburg, VA 24061 USA
来源
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2022年
基金
美国国家科学基金会;
关键词
Quantum machine learning (QML); federated learning (FL);
D O I
10.1109/ICASSP43922.2022.9746622
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore complex machine learning problems. Recently, some QML models were proposed for performing classification tasks, however, they rely on centralized solutions that cannot scale well for distributed quantum networks. Hence, it is apropos to consider more practical quantum federated learning (QFL) solutions tailored towards emerging quantum networks to allow for distributing quantum learning. This paper proposes the first fully quantum federated learning framework that can operate over purely quantum data. First, the proposed framework generates the first quantum federated dataset in literature. Then, quantum clients share the learning of quantum circuit parameters in a decentralized manner. Extensive experiments are conducted to evaluate and validate the effectiveness of the proposed QFL solution, which is the first implementation combining Google's TensorFlow Federated and TensorFlow Quantum.
引用
收藏
页码:8617 / 8621
页数:5
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