论文标题
Grover驱动的量子近似优化算法的问题大小的独立角度
Problem-Size Independent Angles for a Grover-Driven Quantum Approximate Optimization Algorithm
论文作者
论文摘要
量子近似优化算法（QAOA）要求确定电路参数，该电路参数允许从高质量解决方案中采样以组合优化问题。可以使用昂贵的外环优化程序和对量子计算机的重复调用或通过分析方法重复调用此类参数。在这项工作中，我们证明，如果人们知道概率密度函数描述了问题的目标函数是如何分布的，那么可以在Grover驱动的，QAOA备案状态下对这种问题的期望进行计算，可以独立执行系统大小。这样的计算可以帮助您洞悉QAOA中角度的性能和可预测性，尤其是大型问题大小的极限，特别是对于数字分配问题。
The Quantum Approximate Optimization Algorithm (QAOA) requires that circuit parameters are determined that allow one to sample from high-quality solutions to combinatorial optimization problems. Such parameters can be obtained using either costly outer-loop optimization procedures and repeated calls to a quantum computer or, alternatively, via analytical means. In this work we demonstrate that if one knows the probability density function describing how the objective function of a problem is distributed, that the calculation of the expectation of such a problem Hamiltonian under a Grover-driven, QAOA-prepared state can be performed independently of system size. Such calculations can help deliver insights into the performance of and predictability of angles in QAOA in the limit of large problem sizes, in particular, for the number partitioning problem.
