Lately, read about Julia Langs' preference in some quantum research. In general, for single shot computations like matrix and vector operations including linear algebra, the performance between Julia and NumPy is comparable due to NumPy's underlying C/Fortran code base. But for Monte Carlo methods for-loops are hard to avoid. Is there any strategy for For-loops in NumPy avoiding python overhead? Or migrating to Julia is a better choice? I am trying to avoid Rust as I see it as more system level language.