<![CDATA[The background of this research focuses on the challenges of investment portfolio optimization, which often requires long computing time and high complexity, especially with many assets that must be analyzed. The use of quantum algorithms for investment optimization promises a faster and more efficient solution. The purpose of this study is to develop a hybrid quantum algorithm that can combine quantum and classical computing methods to improve portfolio optimization performance. The research method used is an experiment by testing a combination of quantum algorithms (such as variational quantum eigensolver, VQE) and classical algorithms to solve portfolio optimization problems using historical market data. The results show that the hybrid quantum algorithm successfully reduces computational time and improves accuracy in choosing the optimal asset combination, by minimizing risk and maximizing portfolio returns. The conclusion of this study is that the hybrid approach has great potential in overcoming the limitations that exist in pure quantum algorithms and can be effectively applied in investment portfolio optimization. Further research is needed to test these algorithms on a larger scale and with more dynamic market data.]]>
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