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.
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.
Recent advancements in quantum computing, alongside successful deployments of quantum communication, hold promises for revolutionizing mobile networks. While Quantum Machine Learning (QML) presents opportunities, it contends with challenges like noise in quantum devices and scalability. Furthermore, the high cost of quantum communication constrains the practical application of QML in real-world scenarios. This paper introduces a noise-aware clustered quantum federated learning system that addresses noise mitigation, limited quantum device capacity, and high quantum communication costs in distributed QML. It employs noise modelling and clustering to select devices with minimal noise and distribute QML tasks efficiently. Using circuit partitioning to deploy smaller models on low-noise devices and aggregating similar devices, the system enhances distributed QML performance and reduces communication costs. Leveraging circuit cutting, QML techniques are more effective for smaller circuit sizes and fidelity. We conduct experimental evaluations to assess the performance of the proposed system. Additionally, we introduce a noisy dataset for QML to demonstrate the impact of noise on proposed accuracy.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.
In this article, we explore two types of distributed quantum machine learning (DQML) methodologies: quantum federated learning and quantum model-parallel learning. We discuss the challenges encountered in DQML, propose potential solutions, and highlight future research directions in this rapidly evolving field. Additionally, we implement two solutions tailored to the two types of DQML, aiming to enhance the reliability of the computing process. Our results show the potential of DQML in the current Noisy Intermediate-Scale Quantum era.
Quantum Machine Learning (QML) is emerging as a promising technology for tackling complex computational challenges, although its practical implementation faces significant obstacles owed to the inherent noise in current quantum devices. This paper presents a hybrid architecture that combines classical and quantum elements for the development and training of QML models under noisy conditions. This research evaluates the impact of noise on superconducting systems through emulation. The study shows that, compared to noise-free configurations, certain noise levels tend to allow for a reduction in the number of qubits, thus simplifying the architecture of the quantum neural network, which has a direct impact on the computational cost and execution times. Experimental validation was performed by applying three biomedical datasets related to breast cancer detection. The experimental findings revealed that the variations in accuracy between noiseless configurations and those subjected to noisy conditions were minimal, with deviations ranging from 0.11% to 1.68%. Additionally, it was observed that the incorporation of noise during training contributed positively to the efficiency of the process for the datasets under test, achieving improvements in training execution times ranging from a factor of 1.61 to 4.39, when the proposed architecture was emulated on Qaptiva 802.
Quantum-enabled learning is increasingly being explored for future communication and networking applications, including distributed sensing, Internet of Things (IoT), and distributed quantum computing. However, existing approaches often face challenges in scalable inference and efficient information processing. To address these limitations, this paper integrates quantum machine learning (QML) with quantum semantic communication (QSemCom) in an end-to-end learning framework. A classical dataset is first mapped to a low-dimensional feature representation and encoded by a variational quantum transmitter that learns task-relevant semantic features. The resulting features are transmitted through a quantum channel and processed by a receiver to perform a downstream classification task. The proposed framework is evaluated using the MNIST dataset and variational quantum neural networks (QNNs) under ideal and noisy quantum-channel conditions. First, a baseline model is trained over an ideal channel and evaluated under increasing depolarizing noise without retraining. A second set of experiments introduces a trainable receiver QNN and jointly optimizes the transmitter and receiver through end-to-end (E2E) training. The results demonstrate that jointly trained quantum semantic transceivers can adapt the transmitted representation to channel impairments and preserve task-relevant information, highlighting the potential of receiver-aware QML for robust inference in quantum semantic communication.
Quantum Machine Learning (QML) frameworks present a new paradigm for data analysis that uses the parallelism and entanglement of quantum computing to facilitate efficient learning. In this paper we introduce a new robust, scalable and hybrid QML framework to address shortcomings of existing models, such as sensitivity to noise, scalability, and interpretability. By combining quantum kernel methods, variational circuits, and hybrid training routines, the framework offers practical data applications in finance, healthcare and cybersecurity. Its scalability and versatility are supported by benchmarks over simulators and true quantum hardware. This work paves the way for future-proof modular quantum-enhanced data analytics systems.