论文标题
在最大切割和Ising旋转模型问题上应用于QAOA的迭代本地搜索和详尽的搜索方法之间的比较
Comparison between the Iterative Local Search and Exhaustive Search methods applied to QAOA in Max-Cut and Ising Spin Model problems
论文作者
论文摘要
在详尽的搜索（ES）和迭代局部搜索（ILS）之间进行了比较。使用量子近似优化算法（QAOA）进行了这种比较。 QAOA由于其在实际量子硬件中实施的潜力以及在优化问题和量子机学习方面的前途而有希望的未来，因此对QAOA进行了广泛的研究。模拟了ES和ILS方法，以确定本地（经典计算机）和真实仿真（IBM量子计算机）中QAOA这些技术的利弊。这些经典方法在QAOA中用于近似最佳切割和ISING自旋模型（ISM）问题中的最佳期望值，这两种口味均具有三种模拟配置，称为：线性，循环和完整（或完整）。
A comparison is made between Exhaustive Search (ES) and Iterative Local Search (ILS). Such comparison was made using the Quantum Approximation Optimization Algorithm (QAOA). QAOA has been extensively researched due to its this potential to be implemented in actual quantum hardware, and its promising future in optimization problems and quantum machine learning. ES and ILS approaches were simulated to determine the pros and cons of these techniques for QAOA in local (classic computer) and real simulations (IBM quantum computer). These classic approaches were used in QAOA to approximate the optimal expected value in Max-Cut and Ising Spin Model (ISM) problems, both of these flavors have three simulated configurations called: linear, cyclic and complete (or full).
