论文标题
增强量子近似优化算法的框架及其参数设置策略
Enhanced Framework of Quantum Approximate Optimization Algorithm and Its Parameter Setting Strategy
论文作者
论文摘要
引入了量子近似优化算法（QAOA）的增强框架，并分析了参数设置策略。增强的QAOA与QAOA一样有效，但具有更大的计算能力和灵活性，并且使用适当的参数可以更快地到达最佳解决方案。此外，根据对该框架的分析，提供了以$ O（1）$成本选择参数的策略。模拟是在随机生成的3个量表的随机生成的3个量表（3-SAT）上进行的，并且可以在迭代中发现最佳解决方案，而迭代的可能性很高，远小于$ O（\ sqrt {n}）$
An enhanced framework of quantum approximate optimization algorithm (QAOA) is introduced and the parameter setting strategies are analyzed. The enhanced QAOA is as effective as the QAOA but exhibits greater computing power and flexibility, and with proper parameters, it can arrive at the optimal solution faster. Moreover, based on the analysis of this framework, strategies are provided to select the parameter at a cost of $O(1)$. Simulations are conducted on randomly generated 3-satisfiability (3-SAT) of scale of 20 qubits and the optimal solution can be found with a high probability in iterations much less than $O(\sqrt{N})$
