Hybrid Quantum Machine Learning Model for Power Allocation in NOMA

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.