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You can find some information on another approach to portfolio optimization on quantum computer in this article: Quantum computational finance: quantum algorithm for portfolio optimization. The authors deal with minimizing risk descibed by function $w^T\Sigma w$, where $w$ is vector of asset weights and $\Sigma$ is a covariance matrix. The minimization is ...
The objective of the portfolio optimization problem is to trade off expected return ($\mu^T x$) with the risk taken ($x^T \Sigma$x). This could be achieved by introducing a constraint on the risk, e.g. $x^T \Sigma x \leq R$, for an acceptable risk level $R$ and then maximize the return under this constraint. However, this is not a QUBO, i.e., it cannot be ...