Quantum-Enhanced Neural Architectures for Real-Time Contextual NLP in Resource-Constrained Environments

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.

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