Application of Quantum Computing in Discrete Portfolio Optimization

This article proposes a new, more efficient quantum algorithm for portfolio optimization and uses quantum combinatorial optimization (QCO) techniques. The recent construction developed in 2021 has sparked the field of financial portfolio optimization through the Quantum Walk Optimization Algorithm (QWOA). We study the complexity and efficiency of quantum optimization algorithms with a special interest in QWOA. The objective is to minimize the risk of investing by having a good combination of assets in the portfolio. We also focus on reducing the number of iterations yet attaining a high-quality resolution through contraction of the solution space that eases computations. We extend QWOA’s idea by constructing a newly outperforming scheme known as ’Quantum Mix Optimization Algorithm (QMOA)’. QMOA algorithm codes are provided for the implementation and simulation of numerical results. In addition, QMOA’s efficiency (better than the existing QCO algorithms) is discussed. For instance, the least QWOA number of computations required to execute the initial state equation is p > 2 while this value is p ≥ 2 in the proposed QMOA