The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.
The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.
The significant rise in wireless communication, driven by sixth-generation (6G) networks, underscores the urgent need for innovative resource allocation methods to achieve efficiency, scalability, and reliability. This survey explores the integration of Quantum Deep Reinforcement Learning (QDRL) as a strategy to address the challenges posed by dynamic and complex wireless environments. By leveraging quantum computing features such as superposition and entanglement, QDRL enhances computational efficiency and mitigates the scalability limitations of classical and deep reinforcement learning (DRL) in high-dimensional optimization. The study reviews recent QDRL approaches for key resource allocation tasks, including power distribution, user scheduling, and channel assignment, and examines quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNN) for improved decision-making in wireless systems. Furthermore, it emphasizes the connection between quantum computing and DRL, highlighting hybrid architectures that balance computational cost, energy efficiency, and scalability. Finally, the survey identifies research gaps and outlines future directions, paving the way for robust QDRL strategies to support next-generation wireless networks.
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.
One of the most promising new directions in quantum computing and artificial intelligence convergence is the Quantum Neural Networks (QNNs) that can potentially revolutionize the manner in which intelligent systems are designed, trained, and implemented. Although traditional deep learning models have proven to be incredibly successful in fields like computer vision, natural language processing and autonomous systems, they are becoming limited by computational bottlenecks, energy use, and scalability constraints when operating on large high-dimensional data. The distinctive principles of quantum mechanics (superposition, entanglement, and quantum parallelism) may be applied to the QNNs to provide a distinct computational paradigm with the ability to execute exponentially complicated computations more effectively than his classical counterparts. This article gives an in-depth analysis of QNNs as the new frontier of artificial intelligence. We start with the description of the theoretical foundations of quantum computation and their realization in the form of neural networks. We then examine state-of-the-art QNN architectures, such as hybrid quantum-classical models that help overcome present quantum hardware limitations in relation to the capabilities of classical machine learning. Some of the upcoming applications are showcased including molecular simulation and drug discovery and financial modeling, optimization, cryptography and intelligent automation. Such applications reveal the revolutionary power of QNNs to tackle the issues that cannot be tackled by traditional systems. Nonetheless, there are also major obstacles on the way to a practical implementation of QNN. Problems that still remain a barrier to widespread adoption include quantum decoherence, noise in near-term quantum devices, and the inability to scale qubit architectures or to train large-scale quantum models. In order to overcome these obstacles, we speak about the current research work on the topic of error correction, variational algorithms, and hybrid system designs. Also we examine the ethical and societal ramifications of quantum-accelerated AI, such as security, data sovereignty, and technological equity globally.
The emergence of sixth-generation (6G) communication networks is expected to revolutionize wireless systems by enabling ultra-high data rates, ultra-low latency, and massive connectivity. Efficient resource management is a critical challenge due to the dynamic and heterogeneous nature of 6G environments. Traditional optimization techniques are insufficient to handle the complexity of such networks, leading to the adoption of artificial intelligence (AI)-driven approaches. This paper presents a comprehensive review of AI techniques for efficient resource management in 6G networks, focusing on hybrid quantum duplet-convolutional neural network (CNN) models. These models integrate quantum computing with classical deep learning to enhance computational efficiency and optimization capability. The study analyzes recent literature, covering deep learning, reinforcement learning, federated learning, and quantum machine learning approaches. A comparative analysis highlights the advantages of hybrid quantum-CNN models in terms of scalability, adaptability, and performance. The paper also discusses emerging trends such as AI-native network design, edge intelligence, and quantum-enhanced optimization. Furthermore, key challenges including computational complexity, energy consumption, and quantum hardware limitations are examined. The findings suggest that hybrid quantum deep learning models are promising for addressing resource management challenges in future 6G networks.
Quantum computing harnesses quantum mechanics principles like superposition and entanglement to process information using qubits, which can represent multiple states simultaneously unlike classical bits. This enables exponential speedups for complex problems such as optimization, cryptography, and simulations that challenge classical computers. Conventional Quantum Neural Network (QNN) frameworks for wireless communications face significant emulation constraints, including exponential resource scaling, barren plateaus, and hardware noise limitations that hinder practical deployment. This paper reframes the existing QNN architecture—featuring variational quantum circuits trained on priority-based CSMA/CA simulation data—as a baseline system, highlighting its emulation-bound performance comparable to traditional neural networks via Euclidean/cosine similarity benchmarking. Key limitations include qubit/layer scaling issues, prolonged training with small batches, and susceptibility to decoherence, restricting scalability for 6G complexities. The proposed advancement transitions to fault-tolerant quantum hardware via Torch Quantum-Qiskit, incorporating distributed QNNs and error mitigation for MAC/physical layer optimizations like MIMO beamforming, channel estimation, interference mitigation, and access control. Benefits encompass exponential computational advantages, enhanced reliability in high-dimensional environments, and seamless edge-cloud integration, enabling superior efficiency over classical methods in future networks. This generalized methodology empowers wireless practitioners with minimal quantum expertise to achieve transformative network performance.
Next-generation wireless communication systems should have very low latency, high reliability, the ability to connect a lot of devices, and better security.Artificial intelligence (AI) is an important part of intelligent network management because it makes channel estimation, spectrum allocation, beamforming, and network optimization more efficient.But classical AI methods have a lot of problems when used on large wireless networks, such as being very hard to compute, using a lot of energy, and not being able to grow.Recently quantum artificial intelligence i.e.QAI which combines quantum computing with machine learning has become a promising way to get around these problems.By using quantum mechanics ideas like superposition, entanglement and quantum parallelism QAI could speed up optimization tasks, make pattern recognition better and make secure communication better.This paper gives a full overview of QAI and how it could be used in the next generation of wireless communication systems.The study talks about the basics of quantum computing, how AI can help wireless networks, and new quantum machine learning models that are being developed.Also, quantum algorithms like Grover's search algorithm and the Quantum Approximate Optimization Algorithm (QAOA) are looked at in relation to problems with optimizing wireless networks.Lastly, we talk about the problems that QAI-enabled wireless communication systems face right now and the research that needs to be done in the future.