Quantum Annealing for Next-Generation MU-MIMO Detection: Evaluation and Challenges

Abstract

Multi-user (MU), multiple-input, multiple-output (MIMO) detection has been extensively investigated, and many techniques have been proposed. However, further performance improvements may be constrained by limitations in classical computation. The motivation for this work is to test whether a machine that exploits quantum principles can offer improved performance over conventional detection approaches. This paper presents an evaluation of MIMO detection based on quantum annealing (QA) when run on an actual QA quantum processing unit (QPU) and describes the challenges and potential improvements. The evaluations show promising results in some cases, such as near-optimality in a QPSK-modulated 8×8 MIMO case, but poor results in other cases, such as for larger systems or when using 16-QAM. We show that some challenges of QA detection include dealing with integrated control errors (ICE), the limited dynamic range of QA QPUs, an exponential increase in the number of qubits to the problem size, and a high computation overhead. Solving these challenges could make QA-based detection superior to conventional approaches and bring a new generation of MU-MIMO detection methods.

Warm and Cold Start Quantum Annealing for Metaverse Resource Optimization

“…A recent research attempted to solve the spectrum resource allocation problem as a discrete Markov decision process (MDP) using Quantum Reinforcement Learning [24]. Apart from Metaverse, a plethora of research studies have been conducted using QA on traditional cellular networks, such as, next-generation quantumenabled multiple-input multiple-output (MIMO) processing [25], routing [26], scheduling/optimization for Internet of Things (IoT) networks [27] [28], resource optimization for Network Function Virtualization (NFV) [29], and so forth. All the quantum inspired optimization research struggle to simulate large-scale NP-hard problems due to the limited availability of freely available qubits (quantum resources) in today's world [29].…”

The quantum internet: A synergy of quantum information technologies and 6G networks

“…Recent works have shown promising results for quantum algorithms enhanced MIMO data detection in centralised radio access networks (C-RANs) [160]. Some other recent works have also investigated quantum-inspired and quantum annealing algorithms for near-optimal MIMO data detection [161][162][163].…”

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

“…Furthermore, the authors of [136] reported the utilization of properly adjusted noise in quantum systems to enhance the performance of quantum annealing methodologies, specifically in relation to convergence time. Nonetheless, it should be noted that a higher noise level in quantum annealing could compound errors in certain wireless applications, such as multi-user MIMO detection [137]. This amplification of errors may, in turn, affect its detection accuracy.…”