用分布式量子核学习提升语音识别，保护数据隐私且效率更高
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition
- 通过量子终端分发计算任务，仅交换参数不共享数据
- 在情感识别数据集上达到与中心化方法相当的准确率
- 适合通信、汽车、金融等对数据隐私要求高的场景
本文提出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提升语音识别性能。该框架解决了集中式量子核学习中的可扩展性与数据隐私问题，通过经典信道连接的量子终端分发计算任务，仅交换模型参数而不共享本地训练数据，从而保障数据隐私并提升计算效率。在基准语音情感识别数据集上的实验表明，CDQKL在分类准确率和可扩展性方面优于集中式和本地量子核学习模型。其分布式特性在隐私保护和计算效率方面具有优势，适用于电信、汽车、金融等数据敏感领域。研究结果表明，CDQKL能有效利用分布式量子计算实现大规模机器学习任务。
原文摘要 · Abstract (English)
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.
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