Quantum neural network with parallel training (called PS-QNN) is presented in this study to optimize wireless resource allocation. Instead of sending the whole dataset, each edge only requires to send the statistical parameters of the dataset; hence reducing the dimension of training data. As a particular case, the proposed PS-QNN is utilized to optimize transmit precoding and power allocation in non-orthogonal multiple access with multiple-input and multiple-output antennas (MIMO-NOMA). Compared to the conventional training method, analysis shows that the proposed parallel training yields a lower complexity, while achieving a comparable sum rate compared to conventional method.
Quantum neural network with parallel training (called PS-QNN) is presented in this study to optimize wireless resource allocation. Instead of sending the whole dataset, each edge only requires to send the statistical parameters of the dataset; hence reducing the dimension of training data. As a particular case, the proposed PS-QNN is utilized to optimize transmit precoding and power allocation in non-orthogonal multiple access with multiple-input and multiple-output antennas (MIMO-NOMA). Compared to the conventional training method, analysis shows that the proposed parallel training yields a lower complexity, while achieving a comparable sum rate compared to conventional method.
This paper proposes a quantum neural network (QNN) to address joint optimization in wireless communication. By utilizing the advantages of quantum entanglement and superposition in quantum computing and machine learning, QNN can solve optimization problems with lower complexity than classical neural networks, due to its parallel processing capabilities. Specifically, this study applies QNN to jointly optimize digital precoder-combiner and power allocation in a multi-user multiple-input multiple-output non-orthogonal multiple access (MU MIMO-NOMA) system. The performance of the QNN is analyzed and presented.
This study exploits a quantum neural network (QNN) for resource allocation in wireless communications. A QNN is presented to reduce time complexity while still maintaining performance. Moreover, a reinforcement-learning- inspired QNN (RL-QNN) is presented to improve the perfor- mance. Quantum circuit design of the QNN is presented to ensure the practical implementation in noisy intermediate-scale quantum (NISQ) computers. For the QNN, the complexity and the number of required qubits are analyzed as well. As a particular use case, the QNN is utilized for user grouping in non-orthogonal multiple access. The results reveal that the QNN schemes have lower complexities and similar performance in terms of the achievable sum rate when compared with that of the classical neural network.
Recent advances in quantum machine learning with its inherent properties of superposition, quantum parallelism, and quantum entanglement, have opened doors of possibilities in solving complex resource allocation problems in digital communication technologies. This study investigates power allocation in non-orthogonal multiple access (NOMA) systems using hybrid quantum neural networks (QNN) and compares with the classical neural network. A dataset is prepared for training the models and analysis of performance of the model is presented. Results show that the hybrid QNN achieves similar validation accuracy, while reducing overfitting and maintaining comparable performance in maximizing the sum rate. Despite fewer number of layers and parameters, the hybrid QNN achieves competitive results, demonstrating the potential of quantum-enhanced models in NOMA power allocation tasks.
The following study introduces FT-QNN, a federated and quantum teleportation –based quantum neural network, utilized to optimize resource allocation for future wireless communications. The proposed FT-QNN consists of edge quantum neural networks (QNNs) and a cloud QNN, while quantum teleportation allows the cloud QNN to obtain the outputs of edge QNNs without requiring prior measurements on the output states, allowing the cloud to process the outputs directly as quantum states. As a particular case to demonstrate its applicability for wireless resource allocation, FT-QNN is then employed to optimize transmit power allocation coefficients in a power domain non-orthogonal multiple access (NOMA)-based system, aiming to maximize the achievable sum-rate. FT-QNN yields lower complexity compared to a distributed QNN scheme without quantum teleportation, while the numerical results also demonstrated that the FT-QNN is capable to achieve a similar sum-rate compared to the scheme without quantum teleportation.
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.
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.