As more smart devices are connected and collecting massive quantities of data, the Internet of Things is growing rapidly. Resource management is another crucial issue since IoT networks are very diverse and often built and rebuilt dynamically. This study introduces a new kind of deep learning model known as the Pyramid Quantum Neural Network (PY-QNN) to solve the problem of resource allocation in Internet of Things systems. PY-QNN builds on quantum computing to improve the accuracy, scalability, and computation performance of Deep Learning. Because of superposition and entanglement, which increase generalization and provide faster convergence, QNNs enhance learning capabilities. The pyramid structure also helps manage the hierarchy of IoT networks. In order to forecast efficient resource assignment and implement this as soon as feasible to lower latency and boost efficiency, PY-QNN uses simulated resource and network requirements. Experimental findings demonstrate that PY-QNN outperforms baseline common deep learning techniques by reducing resource waste and offering online solutions, especially in large and complex IoT networks.
As more smart devices are connected and collecting massive quantities of data, the Internet of Things is growing rapidly. Resource management is another crucial issue since IoT networks are very diverse and often built and rebuilt dynamically. This study introduces a new kind of deep learning model known as the Pyramid Quantum Neural Network (PY-QNN) to solve the problem of resource allocation in Internet of Things systems. PY-QNN builds on quantum computing to improve the accuracy, scalability, and computation performance of Deep Learning. Because of superposition and entanglement, which increase generalization and provide faster convergence, QNNs enhance learning capabilities. The pyramid structure also helps manage the hierarchy of IoT networks. In order to forecast efficient resource assignment and implement this as soon as feasible to lower latency and boost efficiency, PY-QNN uses simulated resource and network requirements. Experimental findings demonstrate that PY-QNN outperforms baseline common deep learning techniques by reducing resource waste and offering online solutions, especially in large and complex IoT networks.
Internet of Things (IoT) is growing at an accelerated pace where more and more connected smart devices are collecting enormous data. The management of resources is another significant issue because IoT networks are often founded and formed dynamically and are extremely diverse. This paper introduces a new type of deep learning model, the Pyramid Quantum Neural Network (Py-QNN,), that is intended for solving the problem of resource allocation in Internet of Things systems. Py-QNN builds on quantum computing for improving Deep Learning’s computation performance, scalability and accuracy. Pyramid structure helps to control the hierarchy of the IoT networks; additionally, QNNs increase learning abilities due to the existence of superposition and entanglement, which in turn increases the generalization capabilities and ensures faster convergence. In this way, Py-QNN utilizes simulated resource and network requirements to anticipate proficient resource assignment, and implement this quickly to minimize delay and maximize efficiency. Experimental findings reveal that Py-QNN yields better performance over baseline traditional deep learning models especially in large and complex IoT networks by averting resource wastage while offering online solutions.
The Internet of Things (IoT) is a global network of millions of devices connected in wireless that exchange data. Multiple data are aiming to be observed through a single platform, and it becomes necessary to evaluate accuracy in order to realize the ideal IoT platform. IoT data collection is more important to meet the growing data analysis in demanding time-sensitive and real-time decision making. There has been a lot of effort involved in developing efficient quantum neural networks due to the success over the traditional neural network. This research proposes a new deep residual learning based quantum–classical neural network (Res-QCNN). The residual structural block is connected to the quantum neural network to examine and provide a training algorithm for analysing the IoT platform. Simultaneously, the benefits and drawbacks of translating quantum idea form deep residual learning are discussed. As a result, training algorithm is performing in the proposed model from beginning to end, similar working of backpropagation in traditional neural networks. When compared to existing, the Res-QCNN completes better when learning a Unitary function and has robustness for noisy data.
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 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.
Wireless communication systems face increasing challenges in resource allocation, latency reduction, scalability, and adaptation because modern networks generate high-dimensional, nonlinear, and rapidly changing data. This work presents a hybrid Quantum Neural Network (QNN)-driven framework for intelligent resource optimization in dynamic wireless communication environments. The proposed framework integrates variational quantum circuits with classical optimization techniques to support adaptive learning and efficient decision-making. A modular architecture consisting of User, Database, and Operator modules enables secure data acquisition, storage, preprocessing, continuous model training, and operator-driven optimization. Quantum Neural Networks are employed as the primary learning mechanism, while hybrid quantum-classical optimization supports stable parameter training. Quantum annealing is incorporated for complex tasks such as channel allocation, bandwidth management, and resource scheduling. The framework is designed to improve allocation accuracy, reduce latency, enhance scalability, reliability, security, and resource utilization. Continuous learning further enables adaptation to changing traffic patterns, user requirements, workloads, and network conditions. Keywords: Quantum Neural Network, Wireless Communication, Resource Optimization, Quantum Computing, Hybrid Quantum-Classical Learning, Resource Allocation.
The fast growth of Internet of Things (IoT) networks has heightened the demand for secure, scalable and energy efficient communications methods. Traditional cryptographic techniques are more susceptible to quantum computing attacks and therefore have pushed the necessity for quantum secure methods. This study presents an energy efficient artificial intelligence assisted quantum secure communication framework designed to scale and provide reliable IoT networks. The framework uses Quantum Key Distribution (QKD) combined with deep learning optimization to improve both relationship and network performance. A self-adaptive AI module applies its lower level of computational resources and dynamically changing network conditions in order to optimize key management, routing decisions, and resource allocation, which reduces both energy consumption and communication latency. The proposed system is intended to support the specific types of constraints faced within an IoT environment. The experimental simulation results demonstrate that the framework improves energy efficiency, increases packet delivery ratio, and decreases latency in IVN (Intelligent Vehicular Networks) by comparison with traditional means of secure communication. In addition, by combining AI and quantum communications, we increase our resilience to threats posed by both classical and quantum forms of attack while providing an added layer of protection for transmitting data between devices in future wireless networks. Additionally, this proposed framework will provide a very scalable and effective method for providing IoT solutions within the environment of 5G mobile networks or later generations.