  The last couple of years have seen an emergence of physics-inspired computing
for maximum likelihood MIMO detection. These methods involve transforming the
MIMO detection problem into an Ising minimization problem, which can then be
solved on an Ising Machine. Recent works have shown promising projections for
MIMO wireless detection using Quantum Annealing optimizers and Coherent Ising
Machines. While these methods perform very well for BPSK and 4-QAM, they
struggle to provide good BER for 16-QAM and higher modulations. In this paper,
we explore an enhanced CIM model, and propose a novel Ising formulation, which
together are shown to be the first Ising solver that provides significant gains
in the BER performance of large and massive MIMO systems, like $16\times16$ and
$16\times32$, and sustain its performance gain even at 256-QAM modulation. We
further perform a spectral efficiency analysis and show that, for a
$16\times16$ MIMO with Adaptive Modulation and Coding, our method can provide
substantial throughput gains over MMSE, achieving $2\times$ throughput for SNR
$\leq25$ dB, and up to $1.5\times$ throughput for SNR $\geq 30$ dB.
