Search Results (58)

Reliable attribution of artificial intelligence (AI)-generated text to a specific large language model (LLM) matters increasingly as LLMs proliferate, yet where quantum machine learning actually stands on this task has, to our knowledge, never been measured systematically. We benchmark the quantum support vector machine (QSVM) for binary attribution between Gemma 3 and Qwen 2.5 on a 5800-sample corpus from paired prompts: 83 configurations sweeping qubit count, regularization, training-set size, feature-map family, and circuit depth under exact, noiseless classical statevector simulation. QSVM validation accuracy plateaus at approximately 88%, whereas a classical support vector machine with a radial basis function kernel reaches approximately 97.8% on the identical fourteen-dimensional inputs: the ceiling belongs to the quantum (fidelity) kernel, not to the input representation. We measure the mechanism: off-diagonal quantum kernel values shrink exponentially with qubit count, the signature of exponential kernel concentration. The same classical model recovers the stylometric attribution fingerprint, showing it belongs to the shared feature pipeline rather than to the quantum kernel. All large-scale headline results generalize to an independent 1000-text test set produced after every design decision was frozen. The study provides a cautionary, reproducible benchmark for quantum kernel natural language processing and outlines an open-set extension as future work. Full article

As the core equipment integrating primary electrical devices with secondary intelligent control units, the control cabinet plays a vital role in smart substations, and its operating reliability is crucial to the security and stability of the entire power grid. In order to solve the problems of low accuracy and insufficient generalization ability of traditional control cabinet fault diagnosis schemes, this paper proposes a fault diagnosis method based on QPSO-KELM. Firstly, the structure of the control cabinet and the current characteristics of the switching coil are introduced. Then, by combining the global optimization capability of the QPSO with the nonlinear feature extraction advantages of the KELM, a fault diagnosis method for the control cabinet is proposed. Finally, the coil current signals collected by the operating mechanism of the integrated primary and secondary switches in the control cabinet are extracted for experimental analysis to validate the proposed model. Compared with the existing techniques, the proposed approach not only raises the diagnostic accuracy but also shortens the convergence time considerably in the fault-diagnosis task of the primary–secondary integrated switch housed in the control cabinet. It achieves a diagnostic accuracy of 97.5% and saves 13.55 ms in a single training time compared with PSO-KELM, providing a new approach for the intelligent operation and upkeep of substation assets. Full article

Quantum Machine Learning (QML) has recently emerged as a promising approach for cybersecurity applications due to its ability to encode data into high-dimensional Hilbert spaces. However, most QML-based intrusion detection systems rely on a single quantum embedding strategy, limiting representation diversity. This paper proposes a Multi-Embedding Quantum Ensemble (ME-QE) framework that combines Angle Embedding and IQP Embedding using concatenation and weighted fusion strategies. The proposed approach is evaluated on the NSL-KDD dataset for binary intrusion detection using stratified 5-fold cross-validation. Experimental results show that the best fusion configuration achieves 94.75% accuracy, while the classical Random Forest baseline reaches 97.50%. The analysis further demonstrates that Angle Embedding contributes more effectively to classification performance than IQP Embedding in this context. In addition, the study highlights important scalability limitations of quantum kernel methods due to the computational cost of pairwise kernel evaluation. The proposed framework provides insights into hybrid quantum–classical intrusion detection and establishes a foundation for future scalable quantum cybersecurity architectures. Full article

High-level quantum chemical methods (MRCI+Q and MRCI+P) combined with large correlation-consistent basis sets (aug-cc-pVnZ, where n = Q, 5, 6), were used to investigate the ground-state potential energy curves (PECs) and surfaces (PESs) of the $X_2$, XO, and $X_{2}O$ systems (with X = F, Cl, Br). For the triatomic systems, the potential energy data obtained from MRCI+Q/aug-cc-pVQZ calculations in two different Jacobi representations were interpolated using the reproducing kernel Hilbert space (RKHS) method to construct smooth and accurate analytical representations of the PESs. This work provides a detailed comparison of the bonding characteristics of these molecules with those reported in previous studies. It lays the groundwork for future investigations of their dynamics and reactivity. Full article

Quantum federated learning (QFL) enables collaborative training of variational quantum circuits across decentralized clients, but client drift under non-IID data distributions degrades standard Federated Averaging (FedAvg). Existing aggregation methods either introduce tunable hyperparameters that depend on unknown data heterogeneity or incur structural costs such as order-dependent error propagation. We propose Kernel-Weighted Aggregation (KWA), a hyperparameter-free aggregation strategy that constructs a non-parametric kernel density estimate over the received client parameter vectors at each communication round. Clients in high-density regions receive greater voting power, and isolated clients are automatically attenuated. The kernel bandwidth is the median of all pairwise squared distances. We evaluate KWA against six state-of-the-art QFL methods and five classical robust baselines on five binary MNIST digit-pair tasks under IID, Dir($0.5$), and Dir($0.1$) distributions with a unified four-qubit benchmark. Under IID data, KWA recovers the performance of FedAvg. Under Dir($0.5$), KWA attains the highest average accuracy ($0.7120$), followed by FedAvg ($0.7093$). The margin at $N=4$ clients is not statistically significant. It grows to $+0.023$ at $N=16$ in the client scaling experiment. Under this distribution, the five classical robust baselines also rank below FedAvg. Under Dir($0.1$), KWA exceeds FedAvg by $0.028$ on average, with a pooled paired t-test $p=0.068$. Larger-circuit and three-class experiments show that the advantage does not yet transfer consistently beyond the four-qubit binary setting. KWA thus provides a hyperparameter-free aggregation strategy that recovers FedAvg under IID conditions, adapts to client drift, and requires no manually tuned hyperparameters. Full article

Quantum machine learning (QML) has emerged as a promising approach for analyzing the complex, high-dimensional data encountered in oncology, yet research in this area remains fragmented. This systematic literature review synthesizes current applications of QML in cancer care. Following PRISMA guidelines, six databases were searched for peer-reviewed English-language studies published between 2020 and 2026. Of the 212 records identified, 49 studies met the inclusion criteria after screening and quality assessment. The findings show that QML research is dominated by classification and detection tasks, while segmentation is beginning to emerge. Breast cancer and brain tumors are the most frequently investigated domains. Hybrid quantum-classical models, particularly quantum kernel methods, quantum neural networks, and quantum convolutional neural networks, are the predominant approaches. The main barriers to adoption are hardware limitations, including quantum noise, limited qubit availability, and reliance on simulators. Overall, QML in oncology remains in its early stages of development, with limited clinical validation, insufficient model interpretability, and little evidence of a clear quantum advantage. Future research should prioritize evaluation on real quantum hardware, larger and more diverse clinical datasets, standardized benchmarking against classical methods, and closer collaboration between computer scientists and oncology experts to facilitate clinical translation. Full article

We develop a residual-adaptive preconditioned quantum pseudo-spectral method for generalised $\psi$-Caputo initial-value problems containing a discrete delay, weakly singular hereditary memory, and nonlinear reaction terms. A $\psi$-fractional Chebyshev basis yields closed-form operational matrices that are exact on the chosen finite spectral space. To make the hereditary term compatible with block encoding, the power-law kernel is approximated by a sum of exponentials and supplemented by an explicit local near-field correction, converting global memory into finitely many local auxiliary modes. A structure-preserving preconditioner controls the condition number, while a residual-adaptive multidomain strategy and damped Newton iteration treat layers and nonlinearities. We prove well-posedness in Mittag–Leffler weighted graph spaces, derive a combined spectral–kernel–residual error estimate, and state the quantum linear-system complexity with explicit block-encoding normalisations and right-hand-side preparation assumptions. Numerical tests show high accuracy for solutions smooth in the $\psi$-coordinate, improved robustness for singular and layered solutions, substantial condition-number reduction, and lower history cost under sum-of-exponentials compression. To evaluate performance, we compare against L1 product integration and Jacobi collocation, systematically quantifying their respective accuracy, computational cost, and conditioning characteristics. Full article

Hybrid quantum–classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum–classical approaches for classifying defects in aluminum TIG welding images and benchmarking their performance against a conventional deep learning model. A convolutional neural network is used to extract compact and informative feature vectors from weld images, effectively reducing the higher-dimensional pixel space to a lower-dimensional feature space. Our first quantum approach encodes these features into quantum states using a parameterized quantum feature map composed of rotation and entangling gates. We compute a quantum kernel matrix from the inner products of these states, defining a linear system in a higher-dimensional Hilbert space corresponding to the support vector machine (SVM) optimization problem and solving it using a Variational Quantum Linear Solver (VQLS). We also examine the effect of the quantum kernel condition number on classification performance. In our second method, we apply angle encoding to the extracted features in a variational quantum circuit and use a classical optimizer for model training. Both quantum models are tested on binary and multiclass classification tasks, and the performance is compared with the classical CNN model. Our results show that while the CNN model demonstrates robust performance, hybrid quantum–classical models perform competitively. This highlights the potential of hybrid quantum–classical approaches for near-term real-world applications in industrial defect detection and quality assurance. Full article

Quantum kernels hold significant promise for achieving computational advantages in quantum machine learning (QML), yet their effectiveness critically depends on the design of expressive and hardware-compatible feature maps, a challenge that is particularly pronounced in Noisy Intermediate-Scale Quantum (NISQ) devices with limited qubits, gate errors, and restricted connectivity. In this work, we propose a hardware-aware methodology for automated quantum kernel design that integrates quantum device characteristics with learning-based evaluation. Specifically, candidate quantum circuits explored within the hardware-aware circuit space are represented as directed acyclic graphs (DAGs) encoding hardware-specific information such as gate operations, qubit interactions, and noise properties, while a dual graph neural network (GNN) predictor is employed to estimate key surrogate metrics, including probability of successful trials (PST) and kernel-target alignment (KTA), enabling efficient and accurate assessment of circuit fidelity and kernel performance to facilitate the identification of task-specific quantum kernels. Furthermore, feature selection is incorporated to reduce input dimensionality and ensure compatibility with near-term devices. Extensive experiments on multiple benchmark datasets, including Credit Card (CC), MNIST-5, and FMNIST-4, demonstrate that our method consistently outperforms existing baselines in classification accuracy, effectively balancing hardware constraints and model expressivity under realistic noise conditions. These results highlight the potential of combining hardware-aware design with deep learning techniques to advance practical quantum kernel methods under hardware-calibrated noisy simulation settings. Full article

The minimum accuracy heuristic provides a training-free way to evaluate quantum feature maps, but its original formulation assumes balanced datasets, requires an exhaustive Pauli-axis scan, and lacks a formal lower-bound interpretation. In this work, we generalize the metric to arbitrary binary datasets and prove that the resulting generalized minimum accuracy, denoted $R_{min}$, is a certified lower bound on the optimal empirical accuracy $R^{*}$ achievable by linear classifiers in the same feature space. To improve scalability, we introduce Monte Carlo axis-selection strategies that estimate $R_{min}$ from random subsets of Pauli-feature axes and derive quantile-coverage guarantees for sampling high-accuracy directions. We validate the framework using exact statevector simulations of an $n=6$ qubit quantum feature map, corresponding to $d=4^6=4096$ Pauli axes, over 30 independent runs on five synthetic datasets. The proposed methods sample as few as 60 axes, produce lower-bound estimates and achieve speedups of approximately $27\times$ to $68\times$ compared with exhaustive evaluation. The results support generalized minimum accuracy as a scalable and theoretically grounded tool for pre-screening quantum feature maps in simulated quantum-kernel workflows. Full article

Objective: Survival prediction in oncology and cardiology requires models that can capture nonlinear prognostic structure while remaining interpretable, calibrated, and clinically safe. This study develops and evaluates a diagnostic-first hybrid quantum–classical framework for right-censored survival analysis. Methods: We introduce KTA-Survival (Kernel-Target Alignment for survival), a pre-training feasibility diagnostic that adapts kernel-target alignment to censored outcomes by comparing a quantum fidelity kernel with a concordance-based survival target kernel. We then propose QResid-Boost (Quantum Residual Boosting), a Cox-LASSO–anchored residual framework in which a variational quantum circuit is trained on martingale residuals through a Quantum-Skip-Residual architecture. A sigmoid-bounded scalar gate, α, constrains the quantum contribution and allows the model to reduce to the classical baseline when the residual signal is uninformative. The framework was evaluated on GBSG2 (German Breast Cancer Study Group 2; n = 686), FLChain (serum free light chain; n = 1500), WHAS500 (Worcester Heart Attack Study; n = 500), and a synthetic Weibull positive-control dataset containing high-frequency periodic interactions. Results: On the GBSG2 hold-out partition, Random Survival Forest achieved the highest concordance (C = 0.7188), followed by the Stacking ensemble (C = 0.7128), Cox-LASSO (C = 0.7019), and QResid-Boost (C = 0.7016). The leading classical and hybrid models did not differ significantly by paired bootstrap testing, whereas all outperformed the pure quantum variants. In the synthetic positive-control cohort, QResid-Boost improved over Cox-LASSO by ΔC = +0.0397, demonstrating that the quantum residual can add value when nonlinear periodic structure remains after the linear baseline. KTA-Survival yielded positive ΔKTA values across the evaluated datasets and correctly identified the regime in which the quantum residual produced its largest measurable gain. Conclusions: The proposed diagnostic-first framework reframes quantum survival modelling as a gated enrichment strategy rather than an unconstrained replacement for classical risk models. In low-dimensional clinical cohorts where linear structure already explains most prognostic signal, the framework behaves conservatively; when residual nonlinear structure is present, it can provide measurable improvement without uncontrolled model drift. Full article

We characterize observability in representation learning through Platonic Projection Structures (PPS), an operator-theoretic framework for analyzing representation accessibility under partial observation. Rather than treating observable outputs as direct reflections of latent representations, PPS models observation as a geometry induced by a self-adjoint positive semidefinite operator acting on a latent Hilbert space. A system is represented as a triple $(\mathcal{H},\mathsf{\Pi },O),$ where $\mathcal{H}$ denotes a latent representation space, $\mathsf{\Pi }⪰0$ is an observation operator, and $O\left(v\right)=⟨v,\mathsf{\Pi }v⟩$ defines an induced scalar observable. The framework characterizes observability through the quotient geometry $\mathcal{H}/ker\left(\mathsf{\Pi }\right),$ which represents equivalence classes of latent states that are indistinguishable under observation. From this perspective, observable behavior is governed not by latent representations themselves, but by the geometry induced through the observation operator. We show that both quantum measurement and representation inference under linear observation models can be formulated within this common operator-theoretic structure while differing in the algebraic properties of their observation operators. Within this perspective, quantum measurement serves primarily as a mathematically canonical example of projection-mediated observability. The correspondence developed in PPS is therefore structural rather than physical. Within the same framework, representation transfer and knowledge distillation can be interpreted as approximate preservation of observable geometry through the intertwining condition $\mathsf{\Phi }{\mathsf{\Pi }}_T\approx {\mathsf{\Pi }}_S\mathsf{\Phi }.$ PPS further reveals a structural limitation of output-based interpretability: latent components contained in $ker\left(\mathsf{\Pi }\right)$ are fundamentally inaccessible from observables generated through the induced observation process. Accordingly, attribution and explanation methods inherit intrinsic constraints imposed by the observation geometry itself. We provide controlled empirical validations demonstrating kernel-invariant observability, projection-induced attribution gaps, and rank-controlled observable geometry in latent representation spaces. Overall, PPS provides a mathematically explicit characterization of observability through operator-induced quotient geometry, offering a unified perspective on representation accessibility, interpretability, and representation transfer. Full article

Edge artificial intelligence (Edge AI) requires real-time inference under stringent constraints on computation, memory, energy, and connectivity. Although training can be offloaded to servers, efficient, high-capacity inference and rapid on-device adaptation remain central challenges. Cloud-based inference offers substantial computational power but depends on connectivity, latency, privacy, and reliability conditions that edge deployments cannot always guarantee. Classical model-compression methods—including quantization, pruning, distillation, and neural architecture search—have extended the feasibility of on-device inference, yet they leave largely unchanged the fundamental cost of the linear-algebraic, sampling, and optimization primitives that dominate modern deep learning. Quantum computing has therefore been proposed as a complementary accelerator for selected AI workloads, with theoretical advantages in linear systems, singular value decomposition, sampling, kernel evaluation, and optimization. This review surveys the emerging field of quantum-accelerated AI for edge systems under a hybrid architectural premise: edge devices remain classical, while quantum processors operate as remote, cloud, MEC, or near-edge accelerators. We synthesize advances across quantum learning models, hybrid optimization methods, hardware and deployment architectures, and quantum-inspired approaches suitable for constrained devices. We also assess the practical barriers that currently separate asymptotic quantum advantage from deployable edge intelligence, including data loading, measurement overhead, noise, latency, and benchmarking gaps. Finally, we outline a staged research roadmap from near-term hybrid workflows to fault-tolerant and integrated quantum-edge architectures. Full article

Lead isotope ratios are frequently used in archaeometric provenance analysis; however, the overlap of isotopic fields within the Andean metallogenic belt complicates reliable provenance determination. This study presents a reproducible fidelity-based kernel method for the unsupervised clustering of Andean lead-isotope data and investigates whether a quantum-mechanical similarity space can reveal geologically significant structures beyond the classical Euclidean partition. A dataset of 1522 measurements of ²⁰⁶Pb/²⁰⁴Pb, ²⁰⁷Pb/²⁰⁴Pb, and ²⁰⁸Pb/²⁰⁴Pb was analyzed using a fidelity-based quantum kernel based on a three-qubit Pauli feature map and compared with classical K-means clustering, Gaussian mixture models, and Ward’s agglomerative clustering under various preprocessing strategies and cluster counts. The optimal quantum kernel setup achieved the highest silhouette score at k = 2. However, because analytical uncertainties were not consistently reported across all the compiled sources, an uncertainty-weighted similarity could not be applied. Geological insights indicate that this binary division separates less radiogenic, arc-related compositions from more radiogenic and thorogenic crustal signatures, a contrast that broadly follows the west-to-east crustal-contamination gradient across the Andes. Conversely, the traditional four-cluster approach provides more detailed subdivisions that align with the previously identified isotopic provinces. The reported separation reflects the geometry of the quantum feature space rather than any hardware-level speed-up, as this work represents only a simulation approach. Overall, these findings support a hierarchical and complementary approach to analyzing Pb isotope origins, in which quantum kernel clustering provides robust large-scale separation and classical clustering enhances regional understanding. Full article

Background: Breast cancer remains a leading cause of cancer-related mortality, and reliable computational decision support is increasingly viewed as a complement to expert pathological assessment rather than a replacement for it. Variational quantum classifiers (VQCs) and Quantum Support Vector Machines (QSVMs) have recently been promoted as candidate models for medical classification, yet most published comparisons rely on internal hold-out validation alone and report only a single point estimate of discrimination, omitting calibration, decision-analytic value, and explainability—three ingredients that any clinically credible model must furnish. Methods: We assembled a complete quantum–classical machine learning pipeline and evaluated it under a deliberately stringent protocol designed to expose, rather than conceal, the limitations of current Noisy Intermediate-Scale Quantum (NISQ)-era models. The analytical hypothesis was conservative and stated in advance; in light of saturated classical baselines on this benchmark, we did not anticipate a quantum advantage in raw discrimination, and we framed the study as a methodological probe rather than as a competition. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (n = 569) for development and an independent Wisconsin Original (WBC) cohort (n = 683) for external validation, we benchmarked five classical learners (XGBoost, LightGBM, CatBoost, RandomForest, RBF-SVM), two quantum models (an eight-qubit VQC implemented in PennyLane and a ZZ-feature-map QSVM implemented in Qiskit), and a stacked hybrid ensemble. The evaluation framework combined Optuna-driven hyperparameter optimisation, internal–external cross-validation, and external validation on the independent WBC cohort. Robustness and interpretability were then probed through circuit depth and embedding rotation ablation, depolarising noise stress tests, learning curve and feature stability analysis, decision curve analysis, and dual SHAP-based explanations covering both a direct tree-based explanation and a quantum surrogate. Reporting followed the TRIPOD + AI guideline. Results: On the internal test partition, RBF-SVM achieved the highest discrimination (AUC = 0.998), with XGBoost, LightGBM, CatBoost, the hybrid ensemble, and the VQC clustering between 0.992 and 0.996; the QSVM with a ZZ-fidelity kernel underperformed substantially (AUC = 0.727). Pairwise tests for correlated ROC curves indicated that most differences among top models were not statistically significant. On the external WBC cohort, model rankings reorganised, as RBF-SVM (AUC = 0.986, 95% CI 0.946–0.997), RandomForest (0.985, 95% CI 0.945–0.996), VQC (0.983, 95% CI 0.942–0.995), and the hybrid ensemble (0.982, 95% CI 0.941–0.995) all retained near-ceiling discrimination with extensively overlapping confidence intervals. Ablation analysis demonstrated that the choice of embedding rotation is decisive—Z-rotation embeddings collapsed VQC performance to chance levels (AUC ≈ 0.50), whereas X- and Y-rotations preserved it. Depolarising noise up to p = 0.10 had a negligible effect on the VQC, and SHAP analyses converged on worst concave points, mean concave points, and worst area as the dominant predictors across both classical and quantum models. Decision curve analysis showed positive net benefit for both classical and hybrid models across the clinically meaningful threshold range, exceeding both the treat-all and treat-none reference strategies throughout. Conclusions: In the present regime, the principal contribution of QML is not raw discrimination—modern classical learners are already at the data ceiling—but the construction of a rigorous, reproducible, externally validated, and interpretable benchmarking framework in which quantum models can be fairly compared with their classical counterparts. Because evaluation was confined to curated benchmark datasets rather than real-world clinical populations, the interpretability and net benefit findings reported here should be read as benchmark-level evidence and not as a demonstration of readiness for clinical deployment. Full article