i solve this classical optimization using docplex . The requirement is that I need to solve it with quantum algorithms. For solving QUBO problems, I could use QAOA. But for solving quadratic programming problems with only continuous variables, I don't know which quantum algorithm I could use. Or maybe this problem could be converted to a QUBO problem and then handled by QAOA. I'm not sure. and is it poussible to convert directely docplex problem to quadratic program and after to QUBO for simple ising hamiltonien ?
Could anyone help me?
I am not at all an expert on this but maybe I can help. I have read somewhere that adiabatic quantum computers can solve certain "quadratic" problems (whatever that means) which contain an integer number of possible solutions and they find the optimal one. For example traveling salesman problem has perhaps an unmanageable large number of possible paths but they are countable: there is an integer number of them, and that is the type of problem that your virtual Python quantum computer (QC) is able to solve. So even though a continuous problem cannot be solved in principle, there might be a practical way to solve it. Your task is probbaly to discretize your cont. variables into small bins and thus coonvert a CV problem into a DV problem. You shlud do it carefully so that you can guarantee that the optimal discrete solution that QC will find is not far from the optimal real solution. I am sorry if this helps you because your teacher's idea was that you figure it out yourself... Still there is a fair amount of work for you to do, so do not despair :D
Mario Stipčević thank you dear mario i have a complicated problem the model that i put as example is just easy one i know that is very complicated to convert continous variables to binary by discritizing but i will see thank you .