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IEEE Wireless Communications and Networking Conference

DCoRE-QML: Communication-Efficient and Robust Distributed Quantum Machine Learning

Himanshu Sahu, Hari Prabhat Gupta

Published Apr 13, 2026• 0 citations
Publisher
DOI
10.1109/wcnc65185.2026.11555708

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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.
This work introduces a novel distributed Quantum Machine Learning (QML) scheme that integrates differential privacy methods to enable secure and efficient classification. The approach begins with the computation of an embedding that, given a seed, is randomly segmented into multiple sub-vectors. Each segment is perturbed with noise to enforce differential privacy and is then reduced in dimensionality via Principal Component Analysis (PCA). This PCA addresses the parameter limitations of QML. The reduced embeddings are distributed to multiple QML centers, where they are processed by a QML classifier. To obtain a final prediction, individual decisions from each center are combined through an aggregation mechanism. All classical information communications between the server and QML centers are encrypted and protected by a Quantum Key Distribution (QKD) network between these nodes. This scheme enables training and inference in distributed QML settings while robustly preserving privacy. Empirical evaluations show that the method maintains competitive accuracy with fewer quantum resources, paving the way for practical and scalable deployments of QML in real-world quantum computing context.