  We propose a multiple-input multiple-output (MIMO) detector based on an
annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our
detector achieves state-of-the-art performance in terms of symbol error rate
(SER) while keeping the computational complexity in check. Indeed, our method
can be easily tuned to strike the right balance between computational
complexity and performance as required by the application at hand. This balance
is achieved by tuning hyperparameters that control the length of the simulated
Langevin dynamic. Through numerical experiments, we demonstrate that our
detector yields lower SER than competing approaches (including learning-based
ones) with a lower running time compared to a previously proposed
\emph{overdamped} Langevin-based MIMO detector.
