  Simulation-based optimization is a widely used method to solve stochastic
optimization problems. This method aims to identify an optimal solution by
maximizing the expected value of the objective function. However, due to its
computational complexity, the function cannot be accurately evaluated directly,
hence it is estimated through simulation. Exploiting the enhanced efficiency of
Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo
simulation, it frequently outpaces classical simulation-based optimization,
resulting in notable performance enhancements in various scenarios. In this
work, we make use of a quantum-enhanced algorithm for simulation-based
optimization and apply it to solve a variant of the classical Newsvendor
problem which is known to be NP-hard. Such problems provide the building block
for supply chain management, particularly in inventory management and
procurement optimization under risks and uncertainty
