  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
$4\times6$ MIMO with 16-QAM using six levels of parallelism with 240 qubits and
$220~\mu$s QA compute time, achieving 2.5--5$\times$ 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 $1024\times1024$
MIMO, significantly outperforming other MIMO detectors, including the
state-of-the-art RA detector classically implemented in the same way.
