Jul 2026· Journal of Multiscale Materials Informatics· Vol 3, pp. 17-24· 0 citations· 28 references
TL;DR
A structured perspective on the current state of QML is provided to identify opportunities in deploy it effectively in solve real-world problems and to identify opportunities in deploy it effectively in solve real-world problems.
Abstract
Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary field that combines quantum computing with machine learning to address complex computational problems. This survey provides a comprehensive overview of the theoretical foundations, unified taxonomy, and key methodologies in QML. We discuss quantum data encoding techniques, variational quantum models, neural-inspired quantum architectures, and quantum data learning approaches, along with their associated challenges and limitations. The survey further examines evaluation strategies, the concept of quantum advantage, and the role of current quantum hardware and software platforms in advancing QML research. In addition, major application domains and the growing importance of trustworthy QML, including interpretability, robustness, and security, are explored. Finally, we highlight open challenges and future research directions to provide insights into the evolving landscape of Quantum Machine Learning.
Ashis Kumar Pati, Rajesh Vayyala, K. Mohanty et al.· IEEE Access· 0 citations
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through efficient circuit execution and adaptive optimization. The framework integrates optimized quantum circuits with classical learning strategies to improve scalability and stability under NISQ conditions. Experimental results on Iris, Wine, and Breast Cancer datasets show that HyQNet achieves an accuracy of 95.1% and F1-score of 94.8%, outperforming variational QNN (92.6%) and quantum SVM (91.2%). It also reduces runtime to 16.9 s compared to 20.5 s for VQNN, while maintaining efficient utilization of 8 qubits. Statistical analysis confirms significance (p < 0.05), and ablation studies validate the contribution of each component. The results demonstrate improved convergence stability and resource efficiency in hybrid quantum learning systems.
Sudheer Reddy K., Hastimal Jangid, Usha Desai· 2026 International Conferenc...· 0 citations
Quantum computing and artificial intelligence (AI) are converging into a distinct research frontier commonly referred to as quantum-enhanced artificial intelligence, or quantum machine learning (QML). This paper presents a conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning. Using a structured narrative-review methodology, the study synthesizes theoretical foundations, algorithmic building blocks (quantum feature maps, variational quantum circuits, quantum kernel methods), and application domains spanning drug discovery, finance, materials science, and natural language processing. The review develops a hybrid quantum-classical architecture model and a complexity-comparison framework contrasting classical algorithms with their quantum counterparts, including Grover's search and Shor's factoring algorithm. Findings indicate that while theoretical speedups are well established, practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware remains constrained by decoherence, barren plateaus, and limited qubit connectivity. The paper contributes a synthesized taxonomy of quantum-enhanced AI methods and an evidence-based research agenda emphasizing error mitigation, hardware-aware ansatz design, and hybrid workload partitioning. The discussion further situates these developments within the broader trajectory of next-generation computing, arguing that near-term value will accrue primarily through hybrid quantum-classical systems rather than fully quantum pipelines. Implications for researchers, industry practitioners, and policymakers are discussed, alongside limitations inherent to a literature-synthesis approach.
Mohammad Wali Khurami, Musawer Hakimi· Buana Information Technology...· 0 citations
Quantum Machine Learning (QML) is an emerging interdisciplinary field at the intersection of quantum computing and machine learning, delivering new era for advancing learning beyond the limitations of conventional computational systems. By leveraging quantum computing, QML introduces fundamentally different representational spaces that may support novel learning dynamics, expressive model architectures, and potential algorithmic advantages. This workshop explores how quantum-based approaches can be meaningfully integrated into modern machine learning and computer graphics pipelines. Rather than positioning quantum computing as a distant theoretical concept, the workshop frames it as an emerging computational substrate with practical relevance for hybrid architectures, quantum-enhanced models, and future learning paradigms. Meanwhile, the workshop will also examine near-term quantum hardware, deployable system design, and rigorous evaluation methods for distinguishing genuine quantum advantage from strong classical baselines. By bringing together researchers from quantum computing, machine learning, and computer graphics, this workshop aims to bridge theory and practice, identify near-term application opportunities, and foster interdisciplinary discussion on the future of scalable and practical QML.
Wei Zhang, Tianming Liu, Yingfeng Wang et al.· Proceedings of the Special I...· 0 citations
This review aimed to explore the integration of Quantum Computing (QC) with Artificial Intelligence (AI) subsets such as Machine Learning (ML) and Deep Learning (DL), addressing the computational demands posed by the exponential growth of visual data. It identifies key challenges such as interdisciplinary complexity, lack of standard benchmarks, scalability, integration barriers, and the theoretical-practical gap in quantum applications. The review systematically examines existing literature on the application of quantum algorithms in areas including image processing, Natural Language Processing (NLP), Transfer Learning (TL), Federated Learning (FL), networking, cybersecurity and the finance sector. It highlights the usage of quantum principles like superposition and entanglement to accelerate computations, optimize models, and enhance data security in ML/DL frameworks. Findings indicate that integrating QC with ML/DL offers faster convergence, improved optimization, secure decentralized learning, and efficient handling of large-scale and complex data. Specific improvements are observed in TL and FL approaches, NLP accuracy, cryptographic robustness, and performance in medical diagnostics and autonomous systems. QC holds transformative potential in enhancing ML/DL capabilities across domains. Despite existing challenges such as error mitigation and integration complexity, its combination with classical learning methods opens new frontiers for research in AI-driven sectors. Future studies should focus on bridging theoretical and application-level gaps while creating standardized evaluation frameworks.
Poornachander I, R. K. Jatoth, S. Pawar· Cognitive Computation· 0 citations
This study systematically evaluates the efficacy of two quantum machine learning algorithms-Quantum Support Vector Machine (QSVM) and Quantum Neural Networks (QNN) on IBM quantum simulation platforms to provide a comprehensive assessment of QSVM and QNN under current Noisy Intermediate-Scale Quantum (NISQ) constraints.
