Jul 2026· Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Frontiers· pp. 1-4· 0 citations
TL;DR
This workshop explores how quantum-based approaches can be meaningfully integrated into modern machine learning and computer graphics pipelines and frames quantum computing as an emerging computational substrate with practical relevance for hybrid architectures, quantum-enhanced models, and future learning paradigms.
Abstract
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.
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) 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) is a new multidisciplinary field that exploits the computational power of quantum computing for machine learning problems. Current research provides an overview of important QML algorithms, such as Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Variational Quantum Eigensolvers (VQE), Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantumclassical computing techniques, which have recently become more popular owing to hardware constraints in the NISQ period. Analysis reveals that QML provides an edge in working with high-dimensional data, optimization, and probabilistic modeling. Applications of the technology include various areas such as diagnostics, drug discovery, financial models, cybersecurity, and quantum cryptography (Quantum Key Distribution, QKD). Nevertheless, QML technology suffers from multiple difficulties, including the lack of scalable qubits, noise and decoherence, inefficient encoding methods, absence of standard benchmarking, and interpretability of models. Moreover, hybrid computing architecture and nascent quantum software development are also barriers.
J. Chen· Recent Research Reviews Jour...· 0 citations
The development of quantum technologies opens new possibilities for the optimization of machine learning (ML) algorithms. In this contribution, we report on the experimental use of quantum computing for training ML algorithms and models designed to be deployed for inference on embedded systems with limited computational resources. Due to the small size of resource-efficient models for embedded systems, this application of quantum computing provides an excellent opportunity to evaluate a technology that is currently being developed in the noisy intermediate-scale quantum era. We develop an approach based on transforming classical supervised learning problems into a discrete form, enabling their solution using quantum annealing (QA) methods. Three models are presented: a binary XNORNet network, a support vector machine (SVM) with a discrete representation of dual coefficients, and a convolutional neural network. All experiments are performed on the MNIST dataset, using classical optimization methods, simulated annealing, and QA on the D-Wave Advantage system. While classical gradientbased methods maintain superior absolute accuracy (up to 95.97% for SVM), the D-Wave system demonstrates a significant reduction in core optimization time, achieving speedups of up to 15.6× compared to classical optimization on a central processing unit when the system overhead is excluded. Preliminary results suggest that while QA entails a slight accuracy and memory requirements penalty, a drastic reduction in active annealing duration serves as a promising strategy to reduce training latency in future embedded-focused ML applications.
Michał Mańkowski, Bartosz Zwoliński, Arkadiusz Lewandowski et al.· International Conference on...· 0 citations
This review of hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.
Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin et al.· 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
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