Quantum-Enhanced Multi-RIS-Empowered Consumer Electronics mmWave IoT NOMA Networks

This study focuses on sum rate maximization in consumer electronics millimeter-wave (mmWave) internet of things (IoT) networks employing non-orthogonal multiple access and assisted by multiple reconfigurable intelligent surfaces. In such networks, resource-constrained edge IoT devices often face challenges related to limited computational capability, dynamic channel conditions, and inefficient resource allocation. To address these limitations, we formulate a sum rate maximization problem and decompose it into two subproblems. First, a device grouping and scheduling subproblem is tackled using a graph-based quantum k-means (Q-means) clustering algorithm, which offers improved scalability and efficiency in managing dense and heterogeneous device deployments. Second, the combiner design and RIS phase-shift optimization subproblem is solved via a hybrid quantum-classical approach with a variational quantum algorithm, enabling efficient exploration of the high-dimensional solution space. By reflecting practical low-resolution RIS hardware and limited on-device processing typical of consumer-grade mmWave IoT electronics, the proposed framework enhances connectivity, throughput, and energy efficiency for enduser devices such as smart wearables, home gateways, and extended reality terminals. Simulation results demonstrate that the proposed quantum-assisted framework significantly outperforms conventional techniques, highlighting the promising role of quantum computing in enhancing intelligent consumer IoT communications for 6G mmWave networks.