论文标题
增强具有多参数与问题无关层的QAOA ANSATZ
Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer
论文作者
论文摘要
量子近似优化算法（QAOA）有望在组合优化领域解决经典棘手的计算问题。越来越多的证据表明，Qaoa Ansatz最初提出的形式并不是最佳的。为了解决这个问题，我们提出了一个称为QAOA+的替代性ANSATZ，该ANSATZ增加了传统的$ P = 1 $ QAOA ANSATZ，并具有附加的多参数问题无关的层。 QAOA+ ANSATZ允许获得高于$ p = 1 $ QAOA的近似值较高，同时将电路深度低于$ P = 2 $ QAOA的近似值，如随机常规图的MaxCut问题上的基准标记。我们还表明，所提出的QAOA+ ANSATZ使用比标准QAOA大量可训练的经典参数，而在大多数情况下，替代QAOAAANSätze的替代性QaoaAnsätze。
The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount of evidence suggests that the originally proposed form of the QAOA ansatz is not optimal, however. To address this problem, we propose an alternative ansatz, which we call QAOA+, that augments the traditional $p = 1$ QAOA ansatz with an additional multiparameter problem-independent layer. The QAOA+ ansatz allows obtaining higher approximation ratios than $p = 1$ QAOA while keeping the circuit depth below that of $p = 2$ QAOA, as benchmarked on the MaxCut problem for random regular graphs. We additionally show that the proposed QAOA+ ansatz, while using a larger number of trainable classical parameters than with the standard QAOA, in most cases outperforms alternative multiangle QAOA ansätze.
