Year: 2025
Type: conference-paper
Abstract: This paper presents a Consensus-based Distributed Quantum Kernel Learning (CDQKL) framework aimed at improving speech recognition through distributed quantum computing. CDQKL addresses the challenges of scalability and data privacy in centralized quantum kernel learning. It does this by distributing computational tasks across quantum terminals, which are connected through classical channels. This approach enables the exchange of model parameters without sharing local training data, thereby maintaining data privacy and enhancing computational efficiency. Experimental evaluations on benchmark speech emotion recognition datasets demonstrate that CDQKL achieves competitive classification accuracy and scalability compared to centralized and local quantum kernel learning models. The distributed nature of CDQKL offers advantages in privacy preservation and computational efficiency, making it suitable for data-sensitive fields such as telecommunications, automotive, and finance. The findings suggest that CDQKL can effectively leverage distributed quantum computing for large-scale machine-learning tasks.
Authors: Kuan-Cheng Chen, Wenxuan Ma, Xiaotian Xu
Institutions: Imperial College London, Zhejiang University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4410772006