Recent advances in quantum technologies pave the way for noisy intermediate-scale (NISQ) devices, where approximation optimization algorithm (QAOA) constitutes a promising candidate demonstrating tangible advantages based on NISQ devices. In this paper, we consider maximum likelihood (ML) detection problem of binary symbols transmitted over multiple-input and multiple-output (MIMO) channel, fin...

We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.