抑制QAOA中不需要的波动和近似量子退火
Suppressing unwanted fluctuations in QAOA and approximate quantum annealing
摘要：
量子近似优化算法（QAOA）的部分灵感来自数字化量子退火。基于这一灵感，我们开发了一些技术来使用通用门模型量子计算机的额外灵活性来减轻波动效应，这些波动效应已知会扭曲量子退火中的搜索空间并导致错误的最小值。我们发现，即使只是增加的能力，采取泡利X测量允许我们修改混合器的角度，以抵消这些影响的缩放混合器项的方式成比例的Fubini研究度量的对角元素。我们发现，在能量景观扭曲的情况下，减轻这些影响可以导致更高的成功概率，并且我们可以使用相同的泡利X测量来确定哪些变量可能容易受到强烈波动的影响。我们介绍的方法的效果是相关的，即使在相对较低的深度的$p=10-20$，这表明我们正在开发的技术可能是相关的，在短期内。此外，由于这些方法依赖于控制的自由度，这是不典型的修改QAOA，我们的方法将与广泛的其他QAOA的创新兼容。我们进一步验证了这些波动效应可以在IonQ Harmony QPU上观察到。
英文摘要：
The quantum approximate optimisation algorithm (QAOA) was partially inspired by digitising quantum annealing. Based on this inspiration, we develop techniques to use the additional flexibility of a universal gate-model quantum computer to mitigate fluctuation effects which are known to distort the search space within quantum annealing and lead to false minima. We find that even just the added ability to take Pauli X measurements allows us to modify the mixer angles to counteract these effects by scaling mixer terms in a way proportional to the diagonal elements of the Fubini-Study metric. We find that mitigating these effects can lead to higher success probabilities in cases where the energy landscape is distorted and that we can use the same Pauli X measurements to target which variables are likely to be susceptible to strong fluctuations. The effects of the methods we introduce are relevant even at relatively low depth of $p=10-20$, suggesting that the techniques we are developing are likely to be relevant in the near term. Furthermore, since these methods rely on controlling a degree of freedom which is not typically modified in QAOA, our methods will be compatible with a wide range of other QAOA innovations. We further verify that these fluctuation effects can be observed on an IonQ Harmony QPU.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
