Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQML is technically feasible only in limited and highly experimental forms: (i) hybrid workflows where an embedded device performs sensing and classical processing while offloading a narrowly scoped quantum subroutine to a remote quantum processing unit (QPU) or nearby quantum appliance, and (ii) early-stage "embedded QPU" concepts in which a compact quantum co-processor is integrated with classical control hardware. A practical bridge is quantum-inspired machine learning and optimisation on classical embedded processors and FPGAs. This paper analyses feasibility from a circuits-and-systems perspective aligned with the academic community, formalises two implementation pathways, identifies the dominant barriers (latency, data encoding overhead, NISQ noise, tooling mismatch, and energy), and maps them to concrete engineering directions in interface design, control electronics, power management, verification, and security. We also argue that responsible deployment requires adversarial evaluation and governance practices that are increasingly necessary for edge AI systems.
Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQML is technically feasible only in limited and highly experimental forms: (i) hybrid workflows where an embedded device performs sensing and classical processing while offloading a narrowly scoped quantum subroutine to a remote quantum processing unit (QPU) or nearby quantum appliance, and (ii) early-stage "embedded QPU" concepts in which a compact quantum co-processor is integrated with classical control hardware. A practical bridge is quantum-inspired machine learning and optimisation on classical embedded processors and FPGAs. This paper analyses feasibility from a circuits-and-systems perspective aligned with the academic community, formalises two implementation pathways, identifies the dominant barriers (latency, data encoding overhead, NISQ noise, tooling mismatch, and energy), and maps them to concrete engineering directions in interface design, control electronics, power management, verification, and security. We also argue that responsible deployment requires adversarial evaluation and governance practices that are increasingly necessary for edge AI systems.
Quantum Machine Learning (QML) represents a transformative intersection of quantum computing and artificial intelligence, promising exponential speed-ups and new capabilities for data-driven tasks. However, the practical realization of QML is hindered by the current limitations of quantum hardware, including scalability constraints, qubit decoherence, and high error rates. Hybrid quantum-classical architectures have emerged as a pragmatic approach, leveraging classical resources alongside quantum processors to mitigate these hardware challenges. A promising direction for advancing QML is the integration of hybrid architectures into cloud computing platforms and mobile-edge environments. This approach enables scalable access to quantum resources, facilitates real-time data processing, and brings quantum-enhanced intelligence closer to end-users and devices. This article reviews the state-of-the-art in QML, focusing on how hybrid models and quantum algorithms are addressing the dual challenges of hardware scalability and architectural integration within cloud and mobile-edge computing contexts. Recent advances are analyzed, benchmark results are highlighted, and future directions for deploying robust, scalable QML systems in distributed computing environments are discussed.
Quantum Machine Learning (QML) merges Quantum Computing (QC) and Artificial Intelligence (AI) to solve problems that are intractable by classical computers. At the same time, domains such as finance, healthcare, manufacturing, and transportation domains are transitioning into Internet of Things (IoT)-enabled cyber physical systems generating streams of heterogeneous data with high dimensionality and volume. This work presents a unified scientometric and qualitative review of QML across these sectors. A PRISMA-based screening is followed by a Keyword Co-occurrence Cluster Analysis (KCCA), a Keyword Burst Analysis (KBA), and a Citation Burst Analysis (CBA) using CiteSpace on a Scopus-derived corpus of literature. An evidence-based Technology Readiness Level (TRL) framework is introduced to assess the technology’s maturity. Our findings indicate that the volume of publications has increased after 2022, with healthcare dominating the volume and manufacturing the impact. Furthermore, a clear trend towards the development of hybrid quantum classical architectures is present across all domains. Security applications such as anomaly detection are the most developed QML entry point to IoT systems. All four domains are at a TRL of 3-4, indicating that QML research is still at the concept-validation stage. Challenges include hardware noise, barren plateaus, absence of standardized benchmarks, and encoding heterogeneous data from IoT devices for training QML applications.
Quantum Machine Learning (QML) improves quantum computational advantage by utilizing quantum mechanics to process and analyse information far faster than using classical techniques. One significant obstacle in the field of QML is the need for quantum hardware that is both fault-tolerant and noise resilient to implement and execute QML algorithms successfully. Overcoming the difficulties of QML can result in significant advancements in computational speed and accuracy. QML would enable the successful resolution of complex problems that are currently intractable using classical computing methods. This paper provides a comprehensive analysis of the limitations of classical Machine Learning (ML) and offers an overview of QML, highlighting its significance and foundational principles. We explore cutting-edge QML algorithms that leverage quantum parallelism and entanglement, specifically designed for Internet of Things (IoT) networks, to perform tasks more efficiently. We also discuss the architecture of a QML-based IoT network, focusing on integrating quantum computing capabilities into the vast network of interconnected IoT devices. Additionally, the paper introduces fresh perspectives on QML's potential applications, especially in IoT, and proposes future research directions. We offer insights into the structure of QML, its potential applications, and the architectural considerations for integrating QML into IoT. This paper is primarily targeted at engineering managers and strategic decision-makers seeking actionable guidance on the adoption of QML in industrial IoT contexts.
Quantum Machine Learning (QML) unites quantum computing with modern AI to achieve potential gains in expressivity, runtime, and sample efficiency. This paper synthesizes advances in quantum feature maps, variational quantum circuits (VQCs), and hybrid quantum-classical workflows deployable on noisy intermediate-scale quantum (NISQ) hardware. We outline design principles for data encoding, ansatz selection, and trainability, and summarize error-aware practices—error mitigation, shot-frugal estimation, and compiler co-design. Using synthetic but instructive experiments, we illustrate scaling behavior, noise sensitivity, end-to-end inference latency across backends, and optimizer convergence in variational training. We conclude with an agenda emphasizing domain-aligned encodings, rigorous benchmarking, and reproducible open science to validate advantage beyond asymptotic claims.
Supervised quantum machine learning (QML) applies quantum resources to classical tasks such as classification and regression. We examine methods feasible on current Noisy Intermediate-Scale Quantum (NISQ) hardware, including variational (parameterized) quantum circuits (VQCs/PQCs), quantum kernel methods, data encodings, and hybrid quantumclassical workflows, and we summarize recent experiments in quantum-native and low-data regimes. We clarify theory on generalization and trainability and identify practical blockers: noise and depth limits, encoding overheads, missing standardized benchmarks, and unsettled conditions for advantage. Our main contribution is a ten-year outlook from 2025 to 2035 that maps hardware progress, error mitigation, benchmarking, quantum data generation, federated learning & privacy, and AutoQML to potential enterprise use cases. The roadmap draws on a qualitative synthesis of publication trends, milestone experiments, prior surveys, and practitioner insights. Near-term gains are most likely in niche applications and hybrid deployments; broader adoption depends on scalable error-corrected hardware, shared benchmarks, interdisciplinary training, and calibrated expectations.
Quantum machine learning (QML) is an emerging interdisciplinary field which incorporates some features of quantum computing with machine learning. Quantum hardware development has sparked research interest for AI scientists because it gives capabilities in high-dimensional data processing and simultaneous operation execution. The investigation within this paper traces the main features of QML by reviewing variational quantum classifiers (VQCs) as well as quantum kernels and hybrid quantum-classical models. VQCs serve as quantum parameterized circuits whose optimization process with classical feedback achieves quantum superiority for classification applications. Quantum kernels expand Hilbert space features to improve traditional kernel methods, thus proving their functionality in quantum feature space. Hybrid approaches unite NISQ hardware with classical systems, which makes QML applicable to real-world applications right now. (Rietsche et al., 2022; Tychola et al., 2023) An analysis explores separate functions and combined effects of these components, which enhance model performance, extend generalization, and boost computational efficiency. The paper discusses necessary knowledge first, along with existing applications, before analyzing them against traditional methods. The paper reviews current QML tools while exploring their operational readiness as well as practical issues and deployment barriers for broader adoption. The paper conducts an in-depth investigation of significant limitations that include hardware noise alongside questions regarding scalability and interpretability. This paper shows how QML will transform machine learning applications through its review of obstacles that must be resolved to achieve its complete potential development. The study presents researchers and practitioners with an extensive comprehension of QML developments and emerging paths for this revolutionary field.