Consensus-based Distributed Quantum Kernel Learning for Speech Recognition

TL;DR

This paper introduces a distributed quantum kernel learning framework for speech recognition that enhances scalability and privacy by distributing computation across quantum terminals, showing competitive accuracy on benchmark datasets.

Contribution

The paper proposes the first consensus-based distributed quantum kernel learning framework for speech recognition, addressing scalability and privacy challenges in quantum machine learning.

Findings

Achieves competitive classification accuracy on speech emotion datasets.

Demonstrates improved scalability over centralized quantum kernel methods.

Maintains data privacy through distributed computation.

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.