This paper explores the application of quantum game theory to optimize cloud resource allocation. By leveraging the principles of quantum mechanics, the proposed framework aims to enhance efficiency, reduce costs, and improve scalability in cloud computing environments. The study introduces a quantum-based game-theoretic model and compares its performance with classical approaches. The results demonstrate significant improvements in resource utilization and decision-making efficiency. While prior works have explored classical game theory and auction-based methods, this study is among the first to implement quantum game theory in a practical cloud computing context, propose a resource allocation mechanism that incorporates both fairness and efficiency while leveraging the computational advantages of quantum systems, and highlight the strategic benefits of quantum entanglement in fostering collaboration between competing entities in cloud environments. This work not only addresses the current limitations of resource allocation but also redefines the possibilities for optimization in complex systems, making a substantial contribution to both quantum computing and cloud resource management domains.

Early and accurate identification of malignant pulmonary nodules is essential to reduce lung cancer mortality, yet segmentation of these nodules in CT scans remains challenging due to their small size, variable morphology, low contrast, and extreme class imbalance. To address these challenges, we propose a privacy-preserving federated learning framework that integrates a hybrid U-Net–Transformer architecture with a customized Dice-Focal loss function. The hybrid model combines residual convolutional blocks for local texture feature extraction with Transformer-based self-attention for capturing global contextual dependencies, while skip connections, Layer Normalization, and Dropout enhance gradient flow and generalization across heterogeneous client datasets. The Dice-Focal loss mitigates class imbalance and improves boundary sensitivity, yielding stable convergence and precise segmentation of solid nodules. Using the LUNA16 dataset, our federated setup demonstrated high segmentation fidelity for nodules between 15mm and 25mm, achieving Dice coefficients up to 0.93 and robust precision-recall trade-offs across clients. These results highlight the framework’s potential for scalable, privacy-preserving lung cancer screening, while also identifying challenges in segmenting part-solid or low-contrast nodules for future methodological improvements.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose diagnosis relies heavily on subjective behavioral assessments. Electroencephalography (EEG) provides an inexpensive and non-invasive method of objective biomarker detection but is plagued by high dimensionality and noise that complicates automated analysis. This article introduces a hybrid Transformer–Quantum Neural Network (QNN) model that combines deep learning and quantum computing to enhance P300 targeted ASD responses from non-targeted responses. The model uses transformer capabilities captures cross-domain correlations and feature-level dependencies across heterogeneous feature sources and the quantum heads introduced via parameterized quantum circuits detects high-order, nonlinear relations in a Hilbert space via entanglement and superposition. Experimental results on the BCIAUT-P300 dataset better classical alternatives with higher diagnostic accuracy at higher accuracy (0.921) and F1-score (0.915). Statistical comparisons confirm that the hybrid approach yields significant improvements in discriminative reliability and diagnostic odds ratio, justifying quantum feature embedding’s expressivity and efficiency. The results bear witness to the feasibility and potential of hybrid quantum–classical systems as potential future intelligent diagnostic devices for ASD responses from EEG signals.