  In this paper, we study the problem of digital pre/post-coding design in
multiple-input multiple-output (MIMO) systems with 1-bit resolution per complex
dimension. The optimal solution that maximizes the received signal-to-noise
ratio relies on an NP-hard combinatorial problem that requires exhaustive
searching with exponential complexity. By using the principles of alternating
optimization and quantum annealing (QA), an iterative QA-based algorithm is
proposed that achieves near-optimal performance with polynomial complexity. The
algorithm is associated with a rigorous mathematical framework that casts the
pre/post-coding vector design to appropriate real-valued quadratic
unconstrained binary optimization (QUBO) problems. Experimental results in a
state-of-the-art D-WAVE QA device validate the efficiency of the proposed
algorithm. To further improve the efficiency of the D-WAVE quantum device, a
new pre-processing technique which preserves the quadratic QUBO matrix from the
detrimental effects of the Hamiltonian noise through non-linear companding, is
proposed. The proposed pre-processing technique significantly improves the
quality of the D-WAVE solutions as well as the occurrence probability of the
optimal solution.
