Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Consensus-based Distributed Quantum Kernel Learning for Speech Recognition

Kuan-Cheng Chen, Wenxuan Ma, Xiaotian Xu

TL;DR

The findings suggest that CDQKL can effectively leverage distributed quantum computing for large-scale machine-learning tasks, making it suitable for data-sensitive fields such as telecommunications, automotive, and finance.

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.

Consensus-based Distributed Quantum Kernel Learning for Speech Recognition

TL;DR

The findings suggest that CDQKL can effectively leverage distributed quantum computing for large-scale machine-learning tasks, making it suitable for data-sensitive fields such as telecommunications, automotive, and finance.

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.
Paper Structure (9 sections, 10 equations, 4 figures, 2 tables)

This paper contains 9 sections, 10 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Classical Kernel Learning and Quantum Kernel Learning
  4. Consensus-based Distributed Quantum Kernel Learning
  5. Experiments and Result Analysis
  6. Experimental Setup
  7. Data Pre-processing
  8. Classification Result and Analysis
  9. Discussion and conclusion

Figures (4)

  • Figure 1: Conceptual Framework of CDQKL for Speech Recognition in a Quantum HPC Distributed System. The diagram shows quantum terminals in a distributed network processing sensitive speech data locally, preserving privacy while exchanging model parameters via classical channels, coordinated by a central Quantum HPC center, to enhance scalability and computational efficiency.
  • Figure 2: Schematic of the CDQKL framework showing distributed quantum feature mapping across QPUs and the consensus-based learning process for enhancing classification using a Quantum Support Vector Machine.
  • Figure 3: 2D projection of training and testing data points for speech emotion recognition, illustrating the challenging separability between "Sad" (-1) and "Surprise" (1) emotions in the feature space.
  • Figure 4: (a) Waveform of the audio signal showing amplitude over time, highlighting silent and active regions. (b) Spectrogram showing the frequency content over time, with color intensity indicating amplitude in decibels.