# Consensus-based Distributed Quantum Kernel Learning for Speech Recognition

arXiv: https://arxiv.org/abs/2409.05770 · HTML: https://mlsanity.com/paper/2409.05770/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [The Dawn of Quantum Natural Language Processing](https://mlsanity.com/paper/2110.06510/) (2021)
- [Shadows of quantum machine learning](https://mlsanity.com/paper/2306.00061/) (2023)
- [PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection](https://mlsanity.com/paper/2210.03221/) (2022)
- [The complexity of quantum support vector machines](https://mlsanity.com/paper/2203.00031/) (2022)
- [Applying QNLP to sentiment analysis in finance](https://mlsanity.com/paper/2307.11788/) (2023)
- [Foundations of Quantum Federated Learning Over Classical and Quantum Networks](https://mlsanity.com/paper/2310.14516/) (2023)
- [Federated Quantum Long Short-term Memory (FedQLSTM)](https://mlsanity.com/paper/2312.14309/) (2023)
- [Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions](https://mlsanity.com/paper/2004.10756/) (2020)
- [Quantum Federated Learning With Quantum Networks](https://mlsanity.com/paper/2310.15084/) (2023)
- [Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardware](https://mlsanity.com/paper/2407.17325/) (2024)
- [Quantum Computing for Climate Resilience and Sustainability Challenges](https://mlsanity.com/paper/2407.16296/) (2024)
- [Quantum-Error-Mitigated Detectable Byzantine Agreement with Dynamical Decoupling for Distributed Quantum Computing](https://mlsanity.com/paper/2311.03097/) (2023)

## Built on by (most cited first, 10 of 10)

- [Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting](https://mlsanity.com/paper/2412.08851/) (2024)
- [Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.08845/) (2024)
- [Toward Large-Scale Distributed Quantum Long Short-Term Memory with Modular Quantum Computers](https://mlsanity.com/paper/2503.14088/) (2025)
- [Quantum Kernel-Based Long Short-term Memory](https://mlsanity.com/paper/2411.13225/) (2024)
- [Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting](https://mlsanity.com/paper/2505.09395/) (2025)
- [Quantum Machine Learning for UAV Swarm Intrusion Detection](https://mlsanity.com/paper/2509.01812/) (2025)
- [Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series](https://mlsanity.com/paper/2511.02301/) (2025)
- [Special-Unitary Parameterization for Trainable Variational Quantum Circuits](https://mlsanity.com/paper/2507.05535/) (2025)
- [Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications](https://mlsanity.com/paper/2601.02432/) (2026)
- [Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design](https://mlsanity.com/paper/2606.20606/) (2026)

## Abstract (arXiv)

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.