Article ID: 2024EDP7254
This paper presents an improved Quantum Approximate Optimization Algorithm variant based on Conditional Value-at-Risk for addressing portfolio optimization problems. Portfolio optimization is a NP-hard combinatorial problem that aims to select an optimal set of assets and their quantities to balance risk against expected return. The proposed approach uses the QAOA to find the optimal asset combination that maximizes returns while minimizing risk, with a focus on the tail end of the loss distribution. An enhanced QAOA ansatz introduced that offers a balance between optimization quality and circuit depth, leading to faster convergence and higher probabilities of obtaining optimal solutions. Experiments are conducted using historical stock data from Nasdaq, optimizing portfolios with varying numbers of stocks. Our method outperforms original QAOA and CVaR-QAOA, particularly as the size of the problem increases. Regardless of the scenario, whether it involves 10, 12, 14, or 16 stocks, the improved CVaR-QAOA consistently converges within 100 iterations or less, whereas the standard QAOA consistently requires 450 iterations or more.
