Among existing technologies, quantum computing presents great potential in natural language processing (NLP), especially in resource-poor environments where the method of deep learning is hampered by the related computational burden. It introduces a Quantum Assisted Hybrid Neural Network (QHNN) framework by combining various quantum enhanced algorithms with very lightweight neural architectures for real-time, minimal resource-consuming contextual NLP processing. For dimensionality reduction, efficient reduction, the proposed architecture uses the Quantum Variational Autoencoders (QVAE), for contextual understanding, Quantum Attention Mechanism (QAM) and for better sequence modeling, Quantum Kernelized LSTM (QK-LSTM). We also present Quantum Aware Federated Learning (QAFL) to perform the distributed model training across the low power edge devices to provide data privacy. Finally, it is evaluated in terms of efficiency, accuracy, and consumption of energy compared to traditional deep learning models on simulated quantum circuits and benchmarked NLP datasets. Results show that QHNN achieves great reductions in computational complexity with high contextual accuracy, and is thus suitable for edge AI application including IoT conversational agent, low latency chatbots, and mobile real time language translation. Quantum-assisted NLP is this study's contribution to the gap in demonstrating the feasibility of quantum computing advancements for practical deployment of quantum computing for real-world AI applications. Recently, the autonomy has also been extended to this problem by hybrid quantum classical models that when combined with near-term quantum hardware still provide significant scaling and adaptability.
Among existing technologies, quantum computing presents great potential in natural language processing (NLP), especially in resource-poor environments where the method of deep learning is hampered by the related computational burden. It introduces a Quantum Assisted Hybrid Neural Network (QHNN) framework by combining various quantum enhanced algorithms with very lightweight neural architectures for real-time, minimal resource-consuming contextual NLP processing. For dimensionality reduction, efficient reduction, the proposed architecture uses the Quantum Variational Autoencoders (QVAE), for contextual understanding, Quantum Attention Mechanism (QAM) and for better sequence modeling, Quantum Kernelized LSTM (QK-LSTM). We also present Quantum Aware Federated Learning (QAFL) to perform the distributed model training across the low power edge devices to provide data privacy. Finally, it is evaluated in terms of efficiency, accuracy, and consumption of energy compared to traditional deep learning models on simulated quantum circuits and benchmarked NLP datasets. Results show that QHNN achieves great reductions in computational complexity with high contextual accuracy, and is thus suitable for edge AI application including IoT conversational agent, low latency chatbots, and mobile real time language translation. Quantum-assisted NLP is this study's contribution to the gap in demonstrating the feasibility of quantum computing advancements for practical deployment of quantum computing for real-world AI applications. Recently, the autonomy has also been extended to this problem by hybrid quantum classical models that when combined with near-term quantum hardware still provide significant scaling and adaptability.
The convergence of quantum computing and artificial intelligence (AI) has introduced innovative opportunities to accelerate deep learning, particularly within decentralized cloud architectures. This study develops an adaptive quantum AI model leveraging hybrid quantum-classical algorithms to optimize deep learning processes such as training, inference, and resource allocation. The proposed model integrates Variational Quantum Circuits (VQCs) and Quantum Approximate Optimization Algorithms (QAOAs), which enable efficient handling of high-dimensional data and complex optimization tasks inherent in distributed environments. By addressing challenges like latency, energy efficiency, and computational overhead, the quantum AI model demonstrates significant performance gains in decentralized cloud systems.Experimental evaluations on benchmark datasets reveal a 40% reduction in training time, a 30% improvement in resource efficiency, and a 20% increase in prediction accuracy compared to classical deep learning frameworks. This study highlights the transformative potential of quantum computing in AI-driven decentralized cloud architectures, offering insights into its application for computationally intensive tasks across industries such as healthcare, finance, and logistics. Future work will focus on refining quantum hardware compatibility, developing quantum error correction methods, and exploring federated learning applications to expand the scope of quantum AI in privacy-preserving and distributed systems.
The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems.
The growing need to use real-time intelligence on edge devices has revealed the constraint of traditional neural network architectures which usually need high-computation power, energy, and latency. To overcome them, the work states a QuantumInspired Neural Network (QiNN) architecture that incorporates the activation (QIA), entangled feature mixing (EFM), and unitary-like transformation (QILT) layers and makes use of amplitude encoding, to obtain the efficiency and accuracy of ondevice learning. The QiNN model proposed has a log-linear computational complex which means that the resource footprint in terms of resources consumed is greatly reduced without making it harder to predict. Experimental analyses of CIFAR-10, HAR and Speech Commands show that QiNN has an accuracy of 94.1, an inference latency of42.7ms, and an energy usage of54.2mJ, which is better than lightweight baselines like MobileNetV2 and EfficientNet-lite. In addition, QiNN has a high AUC of 0.97 which implies the strong discriminative abilities. The latency breakdown analysis shows that the architecture has real-time inference properties that are appropriate in low-power edge platforms. These findings indicate that quantum-inspired design principles can be successfully used to create scalable, energy-efficient, and high-performance AI designs to use in embedded and IoT applications. Future perspectives are adaptive reconfigure system and federated quantum-inspired learning to distributed edge systems.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
The recent developments in the field of deep learning have made Natural Language Processing (NLP) quite useful, but there are still issues of the complexity of computation, energy consumption, and modeling of complex semantics. Representations and optimization techniques that are quantum enhanced are available in quantum computing and provide new possibilities. Hybrid Quantum Neural Networks (HQNNs) combine quantum computational concepts and classical neural networks, allowing the introduction of a new era in intelligent language processing. The chapter provides the basics of quantum computing and neural NLP models and looks at the hybrid methods of semantic representation and learning. The text classification, sentiment analysis, named entity recognition, and machine translation tasks that are fundamental to NLP are examined through a hybrid quantum-neural approach. Such methods as quantum data encoding, quantum feature extraction and hybrid training approaches are described and the issues associated with scalability, noise, and hardware limitations are discussed.
Hybrid quantum-classical models represent a crucial step toward leveraging near-term quantum devices for sequential data processing. We present Quantum Recurrent Neural Networks (QRNNs) and Quantum Convolutional Neural Networks (QCNNs) as hybrid quantum language models, reporting the first empirical demonstration of generative language modeling trained and evaluated end-to-end on real quantum hardware. Our architecture combines hardware-optimized parametric quantum circuits with a lightweight classical projection layer, utilizing a multi-sample SPSA strategy to efficiently train quantum parameters despite hardware noise. To characterize the capabilities of these models, we introduce a synthetic dataset designed to isolate syntactic dependencies in a controlled, low-resource environment. Experiments on IBM Quantum processors reveal the critical trade-offs between circuit depth and trainability, demonstrating that while noise remains a significant factor, observable-based readout enables the successful learning of sequential patterns on NISQ devices. These results establish a rigorous engineering baseline for generative quantum natural language processing, validating the feasibility of training complex sequence models on current quantum hardware.