  The Portfolio Optimization task has long been studied in the Financial
Services literature as a procedure to identify the basket of assets that
satisfy desired conditions on the expected return and the associated risk. A
well-known approach to tackle this task is the maximization of the Sharpe
Ratio, achievable with a problem reformulation as Quadratic Programming. The
problems in this class mapped to a QUBO can be solved via Quantum Annealing
devices, which are expected to find high quality solutions. In this work we
propose a QUBO formulation for the Sharpe Ratio maximization and compare the
results both with the Quantum Computing state-of-the-art model and a classical
benchmark. Under the assumptions that we require for the proposed formulation,
we derive meaningful considerations about the solution quality found, measured
as the value of Sharpe Ratio.

Quantum Physics