Quantum-Assisted ML for Next-Generation Networks

As classical machine learning approaches fundamental limits in training cost and optimization complexity, quantum computing offers a promising complement through superposition, entanglement, and quantum parallelism. This project investigates hybrid quantum-classical learning pipelines that embed variational quantum circuits into classical ML workflows to address large-scale optimization, feature encoding, and resource allocation problems arising in wireless networks and edge intelligence.