QUANTUM AND QUANTUM-INSPIRED COMPUTATION FOR MIMO COMMUNICATIONS IN WIRELESS NETWORKS

Abstract

A central design challenge for future generations of wireless networks is to meet users' ever-increasing demand for capacity, throughput, and connectivity. Recent advances in the design of wireless networks to this end, including the NextG efforts underway, call in particular for the use of Large and Massive multiple input multiple output (MIMO) antenna arrays to support many users near a base station. These techniques are coming to fruition, yielding significant performance gains, spatially multiplexing information streams concurrently. To fully realize MIMO's gains, however, the system requires sophisticated signal processing to disentangle the mutually-interfering streams from each other. Currently deployed linear filters have the advantage of low computational complexity, but suffer from rapid throughput degradation for more parallel streams. Theoretically optimal Maximum Likelihood (ML) processing can significantly improve throughput over such linear filters, but soon becoming infeasible due to its computational complexity and limitations in processing time. The base station’s computational capacity is thus becoming one of the key limiting factors on performance gains in wireless networks. Quantum computing is a potential tool to address this computational challenge. It exploits unique information processing capabilities based on quantum mechanics to perform fast calculations that are intractable by traditional digital methods. This dissertation presents four design directions of quantum compute-enabled wireless systems to expedite the ML processing in MIMO systems, which would unlock unprecedented levels of wireless performance: (1) quantum optimization on specialized hardware, (2) quantum-inspired computing on classical computing platforms, (3) hybrid classical-quantum computational structures, and (4) scalable and elastic parallel quantum optimization. We introduce our prototype systems (QuAMax, ParaMax, IoT-ResQ, X-ResQ) that are implemented on real-world analog quantum processors, experimentally demonstrating their substantial achievable performance gains in many aspects of wireless networks. As an initial guiding framework, this dissertation provides system design guidance with underlying principles and technical details and discusses future research directions based on the current challenges and opportunities observed.