Quantum and quantum-inspired image segmentation lies at the intersection of quantum computing, computer vision, machine learning and medical image analysis. We map the field from 422 Scopus-indexed publications (2018–2025) retrieved with a documented Advanced Search query and screened in three explicit levels. Formal eligibility retained 408 records. A published topical decision rule was then applied in two operationalizations that differ only in the metadata scope they read (observed agreement 82.8%, Cohen’s κ = 0.65); all 197 records in the two agreement-relevant strata were adjudicated at record level, and the 211 records both operationalizations excluded were validated on a random sample of 60. The final topical corpus contains 143 records, and at the sample rate roughly two to three further eligible records remain in the unexamined remainder. Screening changes the headline picture: the retrieved corpus grows at 20.45% a year (28 records in 2018 to 103 in 2025) but the topical corpus at only 10.41% (13 to 26), so about half of the apparent expansion is peripheral retrieval. China, India and the United States lead at every level. Of the 138 method studies, 9 (6.5%) report execution on physical quantum hardware, 43 (31.2%) are gate-based or variational studies run on simulators or with an unstated environment, 12 (8.7%) use annealing, Ising or QUBO formulations, and 74 (53.6%) are quantum-inspired classical algorithms that require no quantum device; 27 (19.6%) combine a classical vision pipeline with a quantum component. Quantum-inspired classical work falls from 78.0% to 29.2% of the period’s method studies while hybrid work rises from 3 to 19 records. All findings describe publication patterns, not segmentation performance or quantum advantage. A versioned data and code package reproduces every reported number.
Quantum and quantum-inspired image segmentation lies at the intersection of quantum computing, computer vision, machine learning and medical image analysis. We map the field from 422 Scopus-indexed publications (2018–2025) retrieved with a documented Advanced Search query and screened in three explicit levels. Formal eligibility retained 408 records. A published topical decision rule was then applied in two operationalizations that differ only in the metadata scope they read (observed agreement 82.8%, Cohen’s κ = 0.65); all 197 records in the two agreement-relevant strata were adjudicated at record level, and the 211 records both operationalizations excluded were validated on a random sample of 60. The final topical corpus contains 143 records, and at the sample rate roughly two to three further eligible records remain in the unexamined remainder. Screening changes the headline picture: the retrieved corpus grows at 20.45% a year (28 records in 2018 to 103 in 2025) but the topical corpus at only 10.41% (13 to 26), so about half of the apparent expansion is peripheral retrieval. China, India and the United States lead at every level. Of the 138 method studies, 9 (6.5%) report execution on physical quantum hardware, 43 (31.2%) are gate-based or variational studies run on simulators or with an unstated environment, 12 (8.7%) use annealing, Ising or QUBO formulations, and 74 (53.6%) are quantum-inspired classical algorithms that require no quantum device; 27 (19.6%) combine a classical vision pipeline with a quantum component. Quantum-inspired classical work falls from 78.0% to 29.2% of the period’s method studies while hybrid work rises from 3 to 19 records. All findings describe publication patterns, not segmentation performance or quantum advantage. A versioned data and code package reproduces every reported number.
Abstract Image segmentation remains one of the most extensively studied problems in computer vision, with applications ranging from medical image analysis and autonomous driving to remote sensing and industrial inspection. While deep learning approaches currently dominate the field, interest in quantum computing has motivated the development of quantum and quantum-inspired segmentation techniques that explore alternative strategies for image representation, optimization, and learning. This survey provides a comprehensive review of quantum and quantum-inspired image segmentation methods within the broader context of modern segmentation research. To establish this context, we first examine the evolution of image segmentation methodologies, covering classical image processing techniques, machine learning approaches, and contemporary deep learning architectures.We then organize the existing quantum image segmentation literature into four categories: quantum-inspired, tensor network-based, hybrid quantum-classical, and fully quantum methods. Across these categories, we analyze computational principles, image encoding and representation strategies, application domains, and datasets. We further compare quantum and quantum-inspired approaches with established segmentation paradigms in terms of supervision, scalability, and hardware requirements. Our analysis indicates that quantum-inspired methods currently represent the most developed direction, while hybrid approaches are emerging and fully quantum methods remain constrained by quantum hardware, data encoding costs, and scalability challenges. By connecting quantum image segmentation with the broader evolution of segmentation research, this survey provides a unified perspective on the field. It identifies current trends, open challenges, and promising future directions for researchers exploring the emerging role of quantum computing in image segmentation.
Modern agriculture generates large, heterogeneous and high-dimensional data streams whose volume and combinatorial complexity increasingly strain classical computation. Quantum and quantum-inspired computing have been proposed as a means of representing and searching such spaces more efficiently. This review synthesises the emergence of quantum-inspired artificial intelligence and computing in agriculture, maps where these methods have been applied, appraises the certainty of the reported evidence, and evaluates the conditions under which the methods are likely to add value over classical approaches. Following the PRISMA 2020 guideline, four bibliographic databases (Scopus, Web of Science, IEEE Xplore and PubMed) were searched for peer-reviewed studies published between 2017 and 2026, supplemented by citation chaining and a constrained grey-literature scan from which preprints reporting a fully specified method and result were also eligible. After removal of 149 duplicates, 478 records were screened on title and abstract, 82 were assessed in full text, and 17 studies met the eligibility criteria and were carried into a thematic, task-oriented synthesis; no quantitative meta-analysis was performed because reported outcomes are not commensurable. The 17 included studies concentrate in five domains including the quantum-inspired optimisation (2 studies), quantum machine learning (5), plant disease detection (3), genomics and bioinformatics (6), and precision-agriculture sensing (1). By task, the corpus clusters on classification (7 studies) and sequence assembly/alignment (6), with optimisation (2), clustering (1) and sensing (1) thinly represented, and prediction/regression and dynamic, time-resolved phenotyping absent (0 studies each). Reported gains are frequently large in headline terms. For example, quantum image-descriptor disease classifiers reporting accuracies near 99% on curated leaf datasets but rest predominantly on simulators or noisy intermediate-scale quantum (NISQ) hardware, curated benchmarks and weak classical baselines, so the certainty of any quantum advantage is low. Quantum-inspired methods are a credible near-term toolset that can run on classical hardware while quantum processors mature, but current evidence is simulation-heavy, weakly baselined and difficult to reproduce. The clearest research gap is dynamic disease and phenotype-progression modelling. The paper outline a roadmap toward Agriculture 5.0, quantum-enabled breeding and quantum phenomics that prioritises temporal, uncertainty-aware modelling supported by stronger baselines and field validation.
Quantum computing is being investigated as a computational approach for medical image analysis, but its potential role in cardiovascular imaging remains unclear. This review examines gate-based, quantum annealing, and hybrid quantum-classical techniques for cardiovascular image enhancement, image reconstruction, image segmentation, image feature extraction, and image-based disease classification, and it treats quantum-inspired classical algorithms as a separate category. The literature was identified through a structured search of four bibliographic databases and one preprint server, using stated eligibility criteria and study-level data extraction. Applications are considered in echocardiography, computed tomography, cardiac magnetic resonance imaging, nuclear imaging, and coronary angiography, and direct cardiovascular evidence is distinguished from methods developed for other medical imaging tasks. One adjacent cardiac study based on chest radiography is reported separately because it falls outside these five modalities. For each study, the imaging data, qubit count, encoding strategy, execution on a simulator or physical hardware, classical baseline, and validation design are tabulated. The review also defines the forms of quantum advantage that a study could claim, quantifies the qubit and state-preparation costs of common image encodings for realistic cardiac image sizes, and compares quantum methods with classical machine learning and deep learning in terms of computational requirements, training, interpretability, and reported performance. The existing evidence is exploratory and relies mostly on small datasets, compressed image representations, and quantum simulators. Some results are encouraging, but no consistent advantage over well-tuned classical approaches has been established. The key obstacles are image encoding, hardware noise, circuit depth, trainability, end-to-end computational cost, and the lack of external validation. Hybrid designs offer a practical setting for testing selected quantum components within current imaging pipelines. Future progress requires reproducible cardiovascular benchmarks, evaluation on physical quantum devices, and staged clinical validation. Quantum computing is therefore an emerging field of study whose role in cardiovascular imaging has to be defined through rigorous technical and clinical comparison.
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).
The design and performance of computer vision algorithms are greatly influenced by the hardware on which they are implemented. CPUs, multi-core CPUs, FPGAs and GPUs have inspired new algorithms and enabled existing ideas to be realized. This is notably the case with GPUs, which has significantly changed the landscape of computer vision research through deep learning. As the end of Moores law approaches, researchers and hardware manufacturers are exploring alternative hardware computing paradigms. Quantum computers are a very promising alternative and offer polynomial or even exponential speed-ups over conventional computing for some problems. This paper presents a novel approach to image segmentation that uses new quantum computing hardware. Segmentation is formulated as a graph cut problem that can be mapped to the quantum approximate optimization algorithm (QAOA). This algorithm can be implemented on current and near-term quantum computers. Encouraging results are presented on artificial and medical imaging data. This represents an important, practical step towards leveraging quantum computers for computer vision.
Objective: To synthesize the state of the art on how quantum computing (QC) and artificial intelligence (AI) intersect, including algorithmic foundations, software/hardware stacks, empirical evidence of advantage, applications, and open challenges. Methods: We executed a structured search (2019 – 20th August 2025) across major journals (Nature portfolio, APS/PR, Elsevier), arXiv, and official framework documentation (Qiskit, Cirq/TFQ, PennyLane; AWS Braket; NVIDIA CUDA-Q). We included peer‑reviewed studies, comprehensive surveys, and official docs; we excluded opinion pieces and non-reproducible claims. Results: The field converges on hybrid quantum‑classical workflows with two dominant AI families: (i) quantum kernel methods and (ii) variational circuits/QNNs. Trainability remains a core challenge (barren plateaus and noise), with mitigation strategies advancing (local costs, layerwise training, engineered dissipation, learned decoders). Frameworks and cloud orchestrators now support end‑to‑end hybrid training. Empirical “utility” demonstrations in physics‑inspired tasks exist, while domain‑level advantage in mainstream ML is unproven and data‑dependent. Conclusions: QC‑AI integration is maturing into reproducible hybrid stacks. Near‑term value lies in quantum kernels with tailored feature maps, physics‑regularized QNNs, simulator‑accelerated pipelines, and AI‑for‑QC (calibration/decoding). Broad ML advantage awaits lower‑noise hardware and quantum‑friendly data embeddings.