Consensus-based Distributed Quantum Kernel Learning for Speech Recognition
This work addresses privacy and efficiency challenges in speech recognition for data-sensitive fields like telecommunications, automotive, and finance, though it appears incremental as it builds on existing quantum kernel learning methods.
The paper tackles scalability and data privacy issues in quantum kernel learning for speech recognition by proposing a distributed framework that exchanges model parameters without sharing local data, achieving competitive classification accuracy and scalability on benchmark datasets.
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.