Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2208.10316 · Communications in computer and information science

Distributed Quantum Machine Learning

Niels M. P. Neumann, Robert S. Wezeman

Published Jan 1, 2022• 14 citations
arXivHTMLPDF
DOI
10.1007/978-3-031-06668-9_20

Related papers

Quantum Machine Learning Dynamic Quantum State

Jincheng Zhang

Quantum Machine Learning (QML) has emerged as a transformative paradigm, offering the potential to revolutionize data analysis and machine learning. This paper explores a novel quantum machine learning framework, "Dynamic Quantum State," designed to address challenges associated with data distribution, noise, and non-linearity. We propose a mechanism leveraging quantum state manipulation to dynamically generate and optimize the search space, leading to smoother data distributions and enhanced resilience to perturbations. The core claim is that this framework enables efficient data handling and effective solution exploration, surpassing the limitations of conventional machine learning approaches.

•Aug 31, 2026•0 citations

Distributed secure quantum machine learning

Yu‐Bo Sheng, Lan Zhou

中国科学通报:英文版•Jun 27, 2017•244 citations

Federated Quantum Machine Learning

Samuel Yen-Chi Chen, Shinjae Yoo

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

arXiv:2103.12010•Entropy•Apr 13, 2021•20 citations

Distributed secure quantum machine learning

Yubo, Sheng Sheng, Lan Lan +1 more

Chinese Science Bulletin•Jan 1, 2017•1 citation

Advances in Quantum Machine Learning

A. S. Wilson

•Sep 1, 2021•0 citations

ε-DQML: A Framework for Distributed Quantum Machine Learning over Noises and Link Failures

Danhua Zhao, Jiayi Meng, Xiaojun Shang

Quantum machine learning (QML) offers a promising approach to learning complex, high-dimensional data distributions. However, scaling QML beyond a single quantum machine requires distributed quantum computing, which introduces additional noise and link failures through quantum communication protocols such as quantum gate teleportation (QGT). In this paper, we present ε-DQML, a simulation-based framework for studying distributed QML under QGT-induced noise and failures. ε-DQML models the state-of-the-art QGT protocol and its induced noise/failure, and supports efficient noise/failure-aware distributed QML training through a hybrid differentiation method. Preliminary studies on contrastive language-image pretraining and network traffic classification show that the impact of QGT-induced noise is task- and noise-level-dependent, motivating further study on noise/failure-aware distributed QML system design.

•Aug 17, 2026•0 citations
Quantum Machine Learning (QML) has emerged as a transformative paradigm, offering the potential to revolutionize data analysis and machine learning. This paper explores a novel quantum machine learning framework, "Dynamic Quantum State," designed to address challenges associated with data distribution, noise, and non-linearity. We propose a mechanism leveraging quantum state manipulation to dynamically generate and optimize the search space, leading to smoother data distributions and enhanced resilience to perturbations. The core claim is that this framework enables efficient data handling and effective solution exploration, surpassing the limitations of conventional machine learning approaches.
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.
Quantum machine learning (QML) offers a promising approach to learning complex, high-dimensional data distributions. However, scaling QML beyond a single quantum machine requires distributed quantum computing, which introduces additional noise and link failures through quantum communication protocols such as quantum gate teleportation (QGT). In this paper, we present ε-DQML, a simulation-based framework for studying distributed QML under QGT-induced noise and failures. ε-DQML models the state-of-the-art QGT protocol and its induced noise/failure, and supports efficient noise/failure-aware distributed QML training through a hybrid differentiation method. Preliminary studies on contrastive language-image pretraining and network traffic classification show that the impact of QGT-induced noise is task- and noise-level-dependent, motivating further study on noise/failure-aware distributed QML system design.