empirical investigation by several groups around the world, not only for scientific and research purposes [@ronnow2014defining; @denchev2016computational], but also for performance evaluation on structured real world optimization challenges [@venturelli2015quantum; @stollenwerk2017quantum; @mott2017solving]. It is expected that as quantum computing devices mature, they’d become available to enterprises for use through dedicated cloud services [@patentQCware]. Optimization problems comprise a large class of hard to solve financial problems, not to mention the fact that many supervised and reinforcement learning tools used in finance are trained via solving optimization problems (minimization of a cost function, maximization of reward). Several proposed applications of the D-Wave machine to portfolio optimization [@rosenberg2016solving; @marzec2016portfolio], dealt with portfolios that were too small to evaluate the scaling of the chosen approach with the problem size. In this paper we go beyond these early approaches and provide an analysis on sufficient data points to infer a scaling and measure a limited speedup with respect a state-of-the-art numerical method based on genetic algorithms. Moreover, we extensively tune the D-Wave machine’s runs using the hybrid method of Reverse Annealing which is available only on the most advanced models to date, and whose usage has never been published before on structured optimization problems.

#### Portfolio 