X-ResQ: Reverse Annealing for Quantum MIMO Detection with Flexible Parallelism
CoRR(2024)
摘要
Quantum Annealing (QA)-accelerated MIMO detection is an emerging research
approach in the context of NextG wireless networks. The opportunity is to
enable large MIMO systems and thus improve wireless performance. The approach
aims to leverage QA to expedite the computation required for theoretically
optimal but computationally-demanding Maximum Likelihood detection to overcome
the limitations of the currently deployed linear detectors. This paper presents
X-ResQ, a QA-based MIMO detector system featuring fine-grained quantum
task parallelism that is uniquely enabled by the Reverse Annealing (RA)
protocol. Unlike prior designs, X-ResQ has many desirable system properties for
a parallel QA detector and has effectively improved detection performance as
more qubits are assigned. In our evaluations on a state-of-the-art quantum
annealer, fully parallel X-ResQ achieves near-optimal throughput (over 10
bits/s/Hz) for 4×6 MIMO with 16-QAM using six levels of parallelism with
240 qubits and 220 μs QA compute time, achieving 2.5–5× gains
compared against other tested detectors. For more comprehensive evaluations, we
implement and evaluate X-ResQ in the non-quantum digital setting. This
non-quantum X-ResQ demonstration showcases the potential to realize ultra-large
1024×1024 MIMO, significantly outperforming other MIMO detectors,
including the state-of-the-art RA detector classically implemented in the same
way.
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