Abstract:

Traditional computing paradigms face performance bottlenecks in addressing the combinatorial optimization and real-time decision-making challenges of future 6G networks. This paper explores a novel quantum-classical hybrid computing solution. It introduces a framework that utilizes a trapped-ion Quantum Processing Unit (QPU) as a dedicated accelerator to solve the 6G Multi-User Multiple-Input Multiple-Output(MU-MIMO) beamforming optimization problem. The framework maps the communication problem to an Ising model, which is then solved by using the Quantum Approximate Optimization Algorithm (QAOA) in a variational loop with a classical computer. Numerical simulations demonstrate that this hybrid approach can achieve rapid decision-making and improve the system sum-rate by 15% ~20% over traditional heuristics. These results highlight the significant potential of hybrid computing to overcome classical limitations and empower future communication networks.

Key words: hybrid computing, quantum computing, 6G communications, beamforming, trapped ion