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

Abstract

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.