  Multiple-Input-Multiple-Output~(MIMO) signal detection is central to every
state-of-the-art communication system, and enhancements in error performance
and computation complexity of MIMO detection would significantly enhance data
rate and latency experienced by the users. Theoretically, the optimal MIMO
detector is the maximum-likelihood (ML) MIMO detector; however, due to its
extremely high complexity, it is not feasible for large real-world
communication systems. Over the past few years, algorithms based on
physics-inspired Ising solvers, like Coherent Ising machines and Quantum
Annealers, have shown significant performance improvements for the MIMO
detection problem. However, the current state-of-the-art is limited to
low-order modulations or systems with few users. In this paper, we propose an
adaptive multi-stage Ising machine-based MIMO detector that extends the
performance gains of physics-inspired computation to Large and Massive MIMO
systems with a large number of users and very high modulation schemes~(up to
256-QAM). We enhance our previously proposed delta Ising formulation and
develop a heuristic that adaptively optimizes the performance and complexity of
our proposed method. We perform extensive micro-benchmarking to optimize
several free parameters of the system and evaluate our methods' BER and
spectral efficiency for Large and Massive MIMO systems (up to 32 users and 256
QAM modulation).
