论文标题
基于量子增强模拟的优化
Quantum-Enhanced Simulation-Based Optimization
论文作者
论文摘要
在本文中，我们引入了一种基于仿真优化的量子增强算法。基于仿真的优化旨在优化一个目标函数，该目标函数在计算上精确评估，因此可以通过仿真进行近似。量子振幅估计（QAE）可以在经典的蒙特卡洛模拟上实现二次加速。因此，在许多情况下，它也可以加快基于模拟的优化。将QAE与量子优化的想法相结合，我们展示了如何不仅可以用于连续，而且可以用于离散优化问题。此外，该算法在说明性问题（例如在风险约束和库存管理方面的价值）上进行了说明性问题。
In this paper, we introduce a quantum-enhanced algorithm for simulation-based optimization. Simulation-based optimization seeks to optimize an objective function that is computationally expensive to evaluate exactly, and thus, is approximated via simulation. Quantum Amplitude Estimation (QAE) can achieve a quadratic speed-up over classical Monte Carlo simulation. Hence, in many cases, it can achieve a speed-up for simulation-based optimization as well. Combining QAE with ideas from quantum optimization, we show how this can be used not only for continuous but also for discrete optimization problems. Furthermore, the algorithm is demonstrated on illustrative problems such as portfolio optimization with a Value at Risk constraint and inventory management.
