用于语音识别的基于共识的分布式量子核学习
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition
AI总结：
提出CDQKL分布式量子核学习框架，在不共享本地数据的前提下经经典信道交换参数，提升语音识别的隐私性、效率与可扩展性。
摘要：
本文提出了一种基于共识的分布式量子核学习（CDQKL）框架，旨在通过分布式量子计算改进语音识别。CDQKL解决了集中式量子核学习中的可扩展性和数据隐私挑战。其方法是将计算任务分布到通过经典信道连接的多个量子终端上。该方法能够在不共享本地训练数据的情况下交换模型参数，从而维护数据隐私并提高计算效率。在基准语音情感识别数据集上的实验评估表明，与集中式和本地量子核学习模型相比，CDQKL实现了具有竞争力的分类准确率和可扩展性。CDQKL的分布式特性在隐私保护和计算效率方面具有优势，使其适用于电信、汽车和金融等数据敏感领域。研究结果表明，CDQKL能够有效利用分布式量子计算完成大规模机器学习任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
