The incorporation of quantum computing into consumer applications transforms their intelligence, security, and digital capabilities, facilitating the resolution of complex problems. Qubits, or quantum bits, are the basic building blocks of quantum information, and these systems use them to execute complex, multidimensional quantum algorithms. These applications surpass conventional methods in secure transmission, processing, and data storage thanks to quantum physics and theory. In real-world situations, this technology enhances the calibre and promptness of issue resolution. Positioning systems, finance, logistics, optimisation, communication security, and gravimetry are examples of Quantum-Enabled Consumer Applications (QECA). Sensitive digital consumer apps, particularly those accessible remotely like banking, healthcare, and navigation, must be protected by quantum-safe technologies. Safeguarding customer data, goods, and services is essential as QECA grows. The critical function of customer data emphasises how important it is to use and consider when making judgements inside quantum application services.

Even with the advent of cryptography techniques like quantum random number generators, Shor's algorithm, and quantum key distribution (QKD), protecting private information and identity in remote consumer applications is still a difficult undertaking. Insufficient security protocols have led to increasingly complex attacks, compromised data, damage to one's reputation, and grave repercussions for application assets. Therefore, it is imperative to ensure high-quality security through enhanced preventive measures, higher efficiency, reliable monitoring systems, and creative techniques. Novel approaches to safeguarding Quantum-Enabled Consumer Applications have been made possible by the advent of advanced learning techniques including Artificial Intelligence (AI), Federated Learning (FL), Deep Learning (DL), and Machine Learning (ML). These approaches are combined to give QECA strong tools for unearthing knowledge and information that is essential for identifying risks, vulnerabilities, and assaults. Moreover, these technologies make it possible for consumer apps to perform complex calculations and analyse enormous amounts of vital data, greatly enhancing their security and resilience.

This Special Section (SS), named “Advanced Learning Intelligence in Quantum-Enabled Consumer Applications,” explores advanced intelligence solutions that improve QECA security and privacy. The goal is to combine cutting-edge research and ongoing investigations in this dynamic subject. This SS looks at current security taxonomies and practices through the prism of advanced learning intelligence. Researchers, developers, and industry specialists were invited to contribute their novel theoretical and experimental findings. Six papers were chosen for inclusion following a thorough peer review procedure. A summary of each paper is included below.

In [A1], Zhai et al. proposed a region-aware quantum network that pays close attention to learning from the crowd region. The feature extractor, object region awareness module, quantum-driven calibration module, and decoder module are the four main parts of RAQNet. The local information extraction function of the cascaded ORA modules efficiently handles background interference. Two QDC modules are also included to record global data, with features calibrated using quantum states. Comprehensive test outcomes on four crowd benchmark datasets and three cross-domain datasets show that RAQNet performs better than the most advanced rivals in terms of subjective and objective metrics.

Using graph and hypergraph theory, Cao et al. [1] first simulate the DT-empowered software 6G networks and customized slices. Next, under the moniker DT-Slice-Soft-6G, they create a single software resource management architecture. One practical and fine-grained slice resource allocation technique (Heu-DT-Slice-6G) is designed and integrated into the DT-Slice-Soft-6G framework. They then delve into the technical aspects of this established algorithm, including its formulation, a description of its temporal complexity, and pseudocodes. They carry out a thorough assessment effort to verify the viability and benefits of our DT-Slice-Soft-6G framework and the suggested algorithm Heu-DT-Slice-6G. The evaluation consists of selecting standard and traditional slice resource allocation strategies.

Don et al. [2] presented a novel method that combines self-supervised learning with variational quantum classifiers and reduces dimensionality using Principal Component Analysis (PCA). Their novel approach guarantees generalisation with minimal training dataset while maintaining patient privacy, which is crucial for medical applications. By leveraging PCA to efficiently reduce dimensionality, VQC can operate with only two Q-bits, surpassing both classical methods and current quantum hardware limitations. The suggested model was tested against linear classification methods using a rangeof publicly available image datasets in order to validate its performance. The suggested model was tested against linear classification methods using a range of publicly available image datasets in order to validate its performance. Using several public picture datasets, the suggested solution was tested against linear classification techniques to determine how effective it was. With 90% accuracy on the PneumoniaMNIST, 90% accuracy on the BreastMNIST, 80% accuracy on the PathMNIST, and 80% accuracy on the ChestMNIST medical datasets suggesting success, the results are exceptional. Furthermore, on the non-medical datasets Hymenoptera Ants and Bees and Kaggle Cats and Dogs, the model achieved 85% and 90% accuracy, respectively.

An ML model inspired by quantum computing was put forth by Awan et al. [3] to guarantee the resilience of network security. To improve its utilization, they have created an AI-driven SDN architecture. Furthermore, they have implemented a Trust Management protocol that authenticates the dependability and credibility of network nodes and service providers.

Wang et al. [4] presented a productive method for channel estimation that enhances estimate accuracy by extracting the support atomic set using the adaptive threshold stage-wise orthogonal matching pursuit. To be more precise, the optimal atomic threshold is chosen using the modified particle swarm optimization algorithm. This allows the threshold to be dynamically adjusted to extract the support set efficiently based on variations in the wireless channel, enhancing the accuracy of channel estimation. Moreover, the iteration-stopping condition is optimized using the residual ratio cutoff method, which helps reduce system complexity.

Taneja and Rani [A2] presented a robust mechanism for dependable communication in automotive networks, which is enabled by quantum computing. A multi-reconfigurable intelligent surface (RIS) vehicular network architecture is suggested, wherein the mobile nodes use several RISs to communicate qubits (quantum bits) to the APs. An approach for RIS selection is proposed using quantum computing to minimize the energy overhead. As a result, network performance can be improved by choosing the best RIS instead of using multiple RISs simultaneously. The suggested model with the RIS selection process has been found to increase the attained rate by 8.43% compared to the system without any selection. Additionally, the fluctuation with the number of RIS reflecting elements (N) indicates that more elements under equal phase shifts can obtain the maximum rate. An improvement of 9.09% in the obtained rate with equal phase shifts is observed with N = 400. Ultimately, the system's performance is assessed for a range of signal-to-noise ratios (SNR) in various channel situations.

ACKNOWLEDGMENT

Special issues like the present one require the support of many people. The Guest Editors would like to express their sincere gratitude to all of the writers who submitted their insightful works as well as to the many excellent anonymous reviewers. The industrial electronics communities are of great importance to the selected contributions. We express our gratitude to Prof. Kim Fung Tsang, Editor-in-Chief of the IEEE Transactions on Consumer Electronics, for his exceptional help and direction in the preparation and completion of this SS. We also thank the IEEE TCE staff for their professional support.