  One of the problems frequently mentioned as a candidate for quantum advantage
is that of selecting a portfolio of financial assets to maximize returns while
minimizing risk. In this paper we formulate several real-world constraints for
use in a Quantum Annealer (QA), extending the scenarios in which the algorithm
can be implemented. Specifically, we show how to add fundamental analysis to
the portfolio optimization problem, adding in asset-specific and global
constraints based on chosen balance sheet metrics. We also expand on previous
work in improving the constraint to enforce investment bands in sectors and
limiting the number of assets to invest in, creating a robust and flexible
solution amenable to QA.
  Importantly, we analyze the current state-of-the-art algorithms for solving
such a problem using D-Wave's Quantum Processor and compare the quality of the
solutions obtained to commercially-available optimization software. We explore
a variety of traditional and new constraints that make the problem
computationally harder to solve and show that even with these additional
constraints, classical algorithms outperform current hybrid solutions in the
static portfolio optimization model.
