  In a multi-user system with multiple antennas at the base station, precoding
techniques in the downlink broadcast channel allow users to detect their
respective data in a non-cooperative manner. Vector Perturbation Precoding
(VPP) is a non-linear variant of transmit-side channel inversion that perturbs
user data to achieve full diversity order. While promising, finding an optimal
perturbation in VPP is known to be an NP-hard problem, demanding heavy
computational support at the base station and limiting the feasibility of the
approach to small MIMO systems. This work proposes a radically different
processing architecture for the downlink VPP problem, one based on Quantum
Annealing (QA), to enable the applicability of VPP to large MIMO systems. Our
design reduces VPP to a quadratic polynomial form amenable to QA, then refines
the problem coefficients to mitigate the adverse effects of QA hardware noise.
We evaluate our proposed QA based VPP (QAVP) technique on a real Quantum
Annealing device over a variety of design and machine parameter settings. With
existing hardware, QAVP can achieve a BER of $10^{-4}$ with 100$\mu$s compute
time, for a 6$\times$6 MIMO system using 64 QAM modulation at 32 dB SNR.
