Abstract Demonstrating the practical advantage of quantum computation remains a long-standing challenge whereas quantum machine learning becomes a promising application that can be resorted to. In this work, we investigate the practical advantage of quantum machine learning in ghost imaging by overcoming the limitations of classical methods in blind object identification and imaging. We propose two hybrid quantum-classical machine learning algorithms and a physical-inspired patch strategy to allow distributed quantum learning with parallel variational circuits. In light of the algorithm, we conduct experiments for imaging-free object identification and blind ghost imaging under different physical sampling rates. We further quantitatively analyze the advantage through the lens of information geometry and generalization capability. The numerical results showcase that quantum machine learning can restore high-quality images but classical machine learning fails. The advantage of identification rate are up to 10% via fair comparison with the classical machine learning methods. Our work explores a physics-related application capable of practical quantum advantage, which highlights the prospect of quantum computation in the machine learning field.
Abstract Demonstrating the practical advantage of quantum computation remains a long-standing challenge whereas quantum machine learning becomes a promising application that can be resorted to. In this work, we investigate the practical advantage of quantum machine learning in ghost imaging by overcoming the limitations of classical methods in blind object identification and imaging. We propose two hybrid quantum-classical machine learning algorithms and a physical-inspired patch strategy to allow distributed quantum learning with parallel variational circuits. In light of the algorithm, we conduct experiments for imaging-free object identification and blind ghost imaging under different physical sampling rates. We further quantitatively analyze the advantage through the lens of information geometry and generalization capability. The numerical results showcase that quantum machine learning can restore high-quality images but classical machine learning fails. The advantage of identification rate are up to 10% via fair comparison with the classical machine learning methods. Our work explores a physics-related application capable of practical quantum advantage, which highlights the prospect of quantum computation in the machine learning field.
The entwining of quantum computing with classical machine learning has proven to be one of the most promising frontiers of artificial intelligence, as it possesses the potential to speed up intricate computations in ways that classical systems will never be able to achieve. This study delves into the theoretical and practical implications of hybrid quantum-classical approaches in machine learning, while also examining their advantages, limitations, and applicability in different domains. Utilizing quantum entanglement and parallelism, hybrid solutions also have the potential to process high-dimensional data, optimize complex functions, and facilitate pattern recognition tasks more efficiently. However, noise, decoherence, and limited qubit connectivity on current hardware pose considerable barriers to practical implementations. The authors discuss hybrid architectures like quantum-enhanced optimizers and variational quantum circuits which include classical pre- and post-processing to alleviate quantum limitations. Moreover, a comparative study is performed to show the effectiveness of hybrid models comparing with classical machine learning state-of-the-art in natural language processing, computer vision and financial modeling. Results indicate that although hybrid quantum–classical approaches provide significant gains in optimization and computational efficiency; these benefits remain limited by hardware limitations and the scalability of quantum algorithms. With these considerations in mind, the paper offers guidance for targeting future research directions in quantum machine learning, including advances in quantum error correction, quantum hardware scalability, and new algorithmic approaches. Additional advancements needed and notably, the study focused on a smaller representation of data Since quantum computing is relatively new and researchers are still exploring its application, it is still early days in terms of machine learning models that are solely, intrinsically quantum.
Abstract The convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies. Overall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. Addressing these limitations will be critical for translating QML from experimental feasibility to routine medical practice (Marengo and Santamato, 2025; Rawas and AlSaeed, 2026).
As the limits of classical machine learning (ML) become increasingly evident—especially in terms of computational scalability and data processing—quantum computing emerges as a promising frontier capable of addressing these challenges. Quantum Machine Learning (QML) represents the intersection of quantum computing and traditional ML, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance learning capabilities, accelerate training, and solve complex problems that are intractable for classical algorithms. This paper provides a comprehensive overview of the evolving field of QML, beginning with the theoretical foundations of quantum computing and its synergy with classical machine learning principles. We explore key QML algorithms, including quantum support vector machines (QSVM), variational quantum classifiers (VQC), quantum neural networks (QNN), and hybrid quantum-classical approaches that exemplify the practical integration of both paradigms. We further examine real-world applications of QML across domains such as drug discovery, finance, climate modeling, and optimization, where quantum enhancements have begun to show early but promising results. Despite its theoretical potential, QML faces substantial limitations, including quantum hardware instability, limited qubit scalability, noise sensitivity, and unresolved challenges in quantum data encoding and interpretability. In synthesizing recent advancements and persistent obstacles, this paper highlights the importance of continued interdisciplinary research, algorithmic refinement, and hardware evolution. We also outline future directions that could lead QML from experimental promise to practical utility, including developments in quantum error correction, benchmarking standards, and application-specific quantum architectures
ABSTRACT Classical deep learning achieves high accuracy in image analysis tasks but require large volume of data for generalisation. Medical image datasets are often small and expensive to annotate. Arguably, quantum machine learning (QML) has the following benefits over classical machine learning (ML). (i) Quantum superposition and entanglement allow quantum machines to excel over the computational competence of classical computers. (ii) By parallel processing, QML solves problems faster. (iii) QML produces promising results with limited‐size image datasets and limited‐parameter circuits. The practical advantages of QML over classical methods are still emerging and not consistent across all application domains. Ongoing research in quantum hardware and algorithms are expected to bridge this gap. In this review, we provide an outline of quantum neural networks, quantum convolution neural networks and various hybrid models characterised by continuous learning. We also explore human brain‐inspired quantum neuromorphic computing using quantum spiking neural networks, characterised by learning from discrete neuromorphic spikes. We discuss quantum circuits, used for different medical image applications, from the perspectives of the circuit topology, the numbers of input and measurement qubits and rotation and entanglement gates. Furthermore, we conducted a systematic review of the literature on QML‐based medical image applications, datasets and benchmarks, and the analysis of the research gap separately indicating possible improvement opportunities.
Hybrid quantum-classical algorithms have gained attention as a promising strategy to harness the advantages of both quantum and classical computing for machine learning applications. This review delves into the potential of these algorithms to address the limitations inherent in purely classical or quantum methods, offering a thorough overview of the current landscape in this area. We cover the basics of quantum computing, such as qubits, superposition, and entanglement, and emphasize the challenges that both quantum and classical computing face in the realm of machine learning. Various hybrid frameworks, including quantum-classical neural networks, quantum-enhanced optimization algorithms, and quantum feature maps, are explored in depth. We examine specific hybrid algorithms, like the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA), assessing their potential to enhance data preprocessing, feature selection, model training, and inference. The role of quantum-inspired algorithms in bridging the gap between quantum and classical methods is also discussed. We investigate the scalability of hybrid approaches and their potential to capitalize on future advancements in quantum hardware, while considering implementation challenges such as quantum error correction and noise mitigation. The implications of these algorithms for privacy, security, fairness, and interpretability in machine learning are analyzed. Finally, we consider the potential impact of hybrid quantum-classical algorithms on various industries and their integration with other emerging technologies. This review offers a comprehensive perspective on the current state and future directions of this rapidly evolving field.
Quantum Machine Learning (QML) represents a convergent frontier where quantum computing meets artificial intelligence, offering transformative possibilities for computational challenges. This article explores the fundamental concepts, current applications, and future prospects of QML, examining how it addresses classical computational bottlenecks through quantum mechanical principles like superposition and entanglement. It analyzes core quantum computing architectures including Quantum Neural Networks, Variational Quantum Circuits, and Quantum Kernel Methods, highlighting their potential advantages over classical approaches. The article discusses practical applications across drug discovery, cryptography, and optimization problems while acknowledging significant technical challenges such as quantum decoherence, limited qubit connectivity, and error correction requirements. Through a comprehensive examination of hardware development needs, algorithm design strategies, and application discovery pathways, this article provides insights into the future research directions necessary for achieving practical quantum advantage in machine learning applications.