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I'm trying to do a comparison between the classical way to do portfolio selection with Markovitz and the quantum counterpart. With Markovitz I'm able to generate an output representing the best combination, for a specific criterion, and the weights associated to each share. Using a quantum optimization algorithm I only see the value of the objective function for each combination. How can I evaluate the weights of the shares of the selected portfolio?

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  • $\begingroup$ Hi and welcome to Quantum Computing SE. Could you please add more details? Which algorithm do you use for optimization? Maybe a piece of source code will be helpful. As far as I know, QAOA in Qiskit returns both the objective function value and values of all variables involved. $\endgroup$ Jun 4 at 6:04

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