The rapid development of quantum computation has brought new possibilities to many fields. Especially in finance, quantum computing offers significant advantages. Recently, the portfolio optimization problem has been solved by a quantum algorithm with a mean-variance model with sparse data. However, the mean-variance model does not match the practice, and furthermore, the data is mostly dense. To fill the gap, we propose the Quantum-Enhanced Portfolio Optimization based on the mean-semi-variance model, where the mean-semi-variance model incorporates an optimized risk definition. The algorithm also effectively reduces the time complexity of solving high-dimensional linear systems and achieves sparsity independence.