This extended abstract outlines our research on quantum and emerging computing systems for next-generation wireless networks. The research aims to leverage quantum and quantum-inspired computation to expedite baseband processing at base stations. We introduce our system design directions and prototype systems that are implemented on analog quantum processors. The prototypes are designed for quantum-accelerated near-optimal multi-user detection processing in MIMO systems that could drastically increase wireless performance for tomorrow's NextG cellular networking standards, as well as in NextG wireless local area networks (LAN).
This extended abstract outlines our research on quantum and emerging computing systems for next-generation wireless networks. The research aims to leverage quantum and quantum-inspired computation to expedite baseband processing at base stations. We introduce our system design directions and prototype systems that are implemented on analog quantum processors. The prototypes are designed for quantum-accelerated near-optimal multi-user detection processing in MIMO systems that could drastically increase wireless performance for tomorrow's NextG cellular networking standards, as well as in NextG wireless local area networks (LAN).
In order to meet mobile cellular users' ever-increasing data demands, today's 4 G and 5 G wireless networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling the carbon footprint and operational costs of such networks remains a long-standing problem among network designers. This paper takes a long view on this problem, envisioning a NextG scenario where the network leverages quantum annealing for cellular baseband processing. We gather and synthesize insights on power consumption, computational throughput and latency, spectral efficiency, operational cost, and feasibility timelines surrounding quantum annealing technology. Armed with these data, we project the quantitative performance targets future quantum annealing hardware must meet in order to provide a computational and power advantage over CMOS hardware, while matching its whole-network spectral efficiency. Our quantitative analysis predicts that with 82.32μs problem latency and 2.68 M qubits, quantum annealing will achieve a spectral efficiency equal to CMOS while reducing power consumption by 41 kW (45% lower) in a Large MIMO base station with 400 MHz bandwidth and 64 antennas, and a 160 kW power reduction (55% lower) using 8.04 M qubits in a CRAN setting with three Large MIMO base stations.
With unprecedented increases in traffic load in today's wireless networks, design challenges shift from the wireless network itself to the computational support behind the wireless network. In this vein, there is new interest in quantum-compute approaches because of their potential to substantially speed up processing, and so improve network throughput. However, quantum hardware that actually exists today is much more susceptible to computational errors than silicon-based hardware, due to the physical phenomena of decoherence and noise. This paper explores the boundary between the two types of computation---classical-quantum hybrid processing for optimization problems in wireless systems---envisioning how wireless can simultaneously leverage the benefit of both approaches. We explore the feasibility of a hybrid system with a real hardware prototype using one of the most advanced experimentally available techniques today, reverse quantum annealing. Preliminary results on a low-latency, large MIMO system envisioned in the 5G New Radio roadmap are encouraging, showing approximately 2-10x better performance in terms of processing time than prior published results.
This paper introduces a pioneering method in the realm of signal processing for Massive Multiple-Input Multiple-Output (MIMO) systems, leveraging the synergistic potential of hybrid quantum-classical computing paradigms. The escalating demand for high data rate communications necessitates the advancement of MIMO technologies. However, the computational complexity associated with signal processing in such systems, especially in scenarios involving large array sizes, presents a significant challenge. This study proposes an innovative framework that integrates quantum computing algorithms with traditional classical methods to address this challenge. The quantum algorithms are specifically designed to expedite matrix operations and optimization tasks that are central to MIMO signal processing, such as channel estimation and precoding. A comparative analysis with purely classical approaches demonstrates a substantial improvement in computational efficiency and latency reduction, while maintaining, and in certain aspects enhancing, the accuracy of signal detection and system throughput. Furthermore, this hybrid approach offers scalability and adaptability to varying channel conditions and system configurations, a crucial feature for next-generation wireless communication systems. The theoretical foundation is complemented by simulation results, showcasing the feasibility and effectiveness of this approach in practical scenarios. This research not only paves the way for advanced signal processing techniques in Massive MIMO systems but also opens new horizons in the application of quantum computing in telecommunications.
Exploiting (near-)optimal MIMO signal processing algorithms in the next generation (NextG) cellular systems holds great promise in achieving significant wireless performance gains in spectral efficiency and device connectivity, to name a few. However, it is extremely difficult to enable optimal processing methods in the systems, since the required computational amount increases exponentially with more users and higher data rates, while available processing time is strictly limited. In this regard, quantum signal processing has been recently identified as a promising potential enabler of the (near-)optimal algorithms in the systems, since quantum computing could dramatically speed up the computation via non-conventional effects based on quantum mechanics. Given existing quantum decoherence and noise on quantum hardware, parallel quantum optimization could accelerate the process even further at the expense of more qubit usage. In this paper, we discuss the parallelization of quantum MIMO processing and investigate a spin-level preprocessing method for relatively finer-grained decomposition that can support more flexible parallel quantum signal processing, compared to the recently reported symbol-level decomposition method. We evaluate the method on the state-of-the-art analog D-Wave Advantage quantum processor.
In order to meet mobile cellular users' ever-increasing network usage, today's 4G and 5G networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling the carbon footprint and operational costs of such networks remains a long-standing problem among network designers. This Challenge paper takes a long view on this problem, envisioning a NextG scenario where the network leverages quantum annealing computation for cellular baseband processing. We gather and synthesize insights on power consumption, computational throughput and latency, spectral efficiency, and operational cost, and deployment timelines surrounding quantum technology. Armed with these data, we analyze and project the quantitative performance targets future quantum hardware must meet in order to provide a computational and power advantage over silicon hardware, while matching its whole-network spectral efficiency. Our quantitative analysis predicts that with quantum hardware operating at a 140 μs problem latency and 4.3M qubits, quantum computation will achieve a spectral efficiency equal to silicon while reducing power consumption by 40.8 kW (45% lower) in a representative 5G base station scenario with 400 MHz bandwidth and 64 antennas, and an 8 kW power reduction (16% lower) using 2.2M qubits in a 200 MHz-bandwidth 5G scenario.
This work presents the implementation of a Quantum-Classical Hybrid Computational Framework on Mobile Wireless Systems, exploring the integration of quantum-inspired algorithms with classical mobile computing architectures. The framework leverages hybrid computational strategies to optimize processing efficiency, data throughput, and resource allocation across distributed wireless networks. By bridging quantum algorithmic principles with practical mobile system constraints, this research demonstrates potential pathways for enhanced AI processing, secure communications, and real-time signal computation in next-generation mobile environments. The study provides both theoretical insights and practical implementations, offering a foundation for future research in hybrid quantum-classical mobile computing.