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 of$\mathbf{42.7 ms}$, and an energy usage of$\mathbf{54.2 mJ}$, 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.
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.
Two strong transformative fields in contemporary artificial intelligence (AI) are quantum computing and deep learning. Neuro-nanophotonic systems also introduce quantum-inspired innovations that enhance the computing power of neural networks. Quantum-Inspired Neural Networks (QINNs) have shown promise as a potential alternative to the classical deep learning models, particularly when processing high-dimensional data and solving complex problems efficiently. QINNs leverage the attributes of quantum mechanics to enable efficient deep learning methodologies, achieving superior performance in AI-based applications. In this research, we present a QINN framework that synergizes the power of Quantum Variational Circuits (QVCs) and Quantum Natural Gradient Descent (QNGD) to improve learning efficiency of learning. Experimental results show that QINNs can achieve 97% accuracy, which is 4.5% improvement over the classical models, and computational time and energy consumption can be reduced by 45% and 53%, respectively. Furthermore, QINNs exhibited greater robustness to adversarial noise, making them better candidates for healthcare, cybersecurity, and large-scale AI applications. The results illuminate the game-changing ability of QINNs with respect to scalable, cross-computational and highly resilient AI architectures.
The blistering development of the Internet of Things (IoT) has brought an urgent necessity to an efficient data processing at the end of the network, where devices are characterized by few computational capabilities, and little to no energy availability. Conventional machine learning (ML) solutions can offer high-performance on a centralized system, but do not provide the edge environments with the required latency and the corresponding performance. This paper gives a quantum machine learning (QML)-backed architecture that is used to optimize IoT edge processing, inspiring quantum feature encoding along with quantum neural networks with classical post-processing resource allocation. Different simulated IoT datasets were tested on the framework and they all represent applications to smart home, industrial IoT, and environment monitoring. According to experimental findings, processing latency (improved by 39 percent), energy consumption (improved by 40 percent), memory reorganization (improved by 40 percent), and prediction accuracy (improved by 6 per cent.) were significantly less than conventional ML models. The results demonstrate that QML models are able to make real-time decisions significant and scalable as well as sustainable in edge computing. Quantum-enhanced edge intelligence can significantly improve IoT data processing speed, efficiency, and smart decision-making.
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.
Quantum-inspired neural networks (QINNs) have emerged as a promising approach to leveraging quantum computational principles for accelerating and enhancing artificial intelligence. However, a significant limitation of existing QINNs lies in their failure to fully capture the essence of quantum entanglement, a cornerstone of quantum mechanics. This paper proposes a novel architecture, termed Entangled Node Representation (ENR) QINNs, which directly incorporates true quantum entanglement into the network structure. The core idea is to represent nodes as entangled qubits, allowing for inherent correlations between nodes that surpass the limitations of classical neural networks. We postulate that this approach will dramatically improve learning efficiency by enabling the network to explore a wider solution space and converge faster. The proposed ENR QINNs introduce a formal mathematical framework for modeling and training these networks, exploring the potential of superposition and interference effects within the network's architecture. The theoretical analysis demonstrates that the ENR QINNs possess the potential to achieve exponential speedups in computation compared to traditional neural networks, particularly in tasks involving complex correlations. We outline key considerations for the design and training of ENR QINNs, including qubit representation choices, entanglement management strategies, and optimization algorithms specifically tailored to exploit the quantum properties of the network. Further research will focus on experimental validation and scaling the architecture for real-world applications.
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.
This paper introduces a novel approach to neural network design, termed Quantum-Inspired Neural Networks (QINNs) with Adaptive Graph Structures. The core idea is to leverage the fundamental principles of quantum mechanics – specifically superposition and entanglement – to create a network architecture that exhibits enhanced information processing capabilities. Traditional neural networks often struggle with complex, non-linear relationships and can suffer from vanishing gradients during training. QINNs address these limitations by employing dynamically evolving graph structures that mimic the interconnectedness and efficient information transfer observed in quantum systems. The network architecture is designed to represent data through a superposition of states, allowing for the exploration of multiple solution paths simultaneously. Furthermore, adaptive graph structures, inspired by entanglement, facilitate the correlation of information between nodes, significantly improving the network's ability to capture intricate patterns. This approach represents a significant departure from simply using quantum concepts as an inspirational element; it's a fundamental integration of quantum principles into the neural network design, leading to potentially superior performance in tasks demanding high computational efficiency and pattern recognition accuracy. The paper outlines the theoretical framework, the proposed architecture, and discusses the potential benefits and future research directions.