  This paper investigates the application of Simulated Bifurcation algorithms
to approximate optimal asset allocations. It will provide the reader with an
explanation of the physical principles underlying the method and a Python
implementation of the latter applied to 441 assets belonging to the S&P500
index. In addition, the paper tackles the problem of the selection of an
optimal sub-allocation; in this particular case, we find an adequate solution
in an unrivaled timescale.
