The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: A Typical Case

The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: A Typical Case
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量子近似优化算法需要看全图:一个典型案例

DOI:
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发表时间:
2020
期刊:
arXiv.org
影响因子:
--
通讯作者:
S. Gutmann
S. Gutmann
中科院分区:
--
文献类型:
--
作者:
E. Farhi;D. Gamarnik;S. Gutmann

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量子近似优化算法自然适用于图上的组合搜索问题。量子电路具有遵守图的局部性的酉运算符的p个应用。在一个有界度的图上,当p足够小时,QAOA输出的状态中的遥远量子比特的测量给出了不相关的结果。我们的重点是在n/2条边保持d固定且n大的随机图中寻找大的独立集。利用随机图中几乎最优独立集的重叠间隙性质和QAOA的局部性,我们能够证明,如果p小于d依赖的常数乘以logn,则QAOA不能比找到一个大小为d的最优值的854倍的独立集更好。因为对数是缓慢增长的,即使在一百万量子比特,我们也只能证明,如果p是个位数,算法就会受阻。在较高的p,该算法“看到”整个图,我们没有迹象表明,性能是有限的。
The Quantum Approximate Optimization Algorithm can naturally be applied to combinatorial search problems on graphs. The quantum circuit has p applications of a unitary operator that respects the locality of the graph. On a graph with bounded degree, with p small enough, measurements of distant qubits in the state output by the QAOA give uncorrelated results. We focus on finding big independent sets in random graphs with dn/2 edges keeping d fixed and n large. Using the Overlap Gap Property of almost optimal independent sets in random graphs, and the locality of the QAOA, we are able to show that if p is less than a d-dependent constant times log n, the QAOA cannot do better than finding an independent set of size .854 times the optimal for d large. Because the logarithm is slowly growing, even at one million qubits we can only show that the algorithm is blocked if p is in single digits. At higher p the algorithm "sees" the whole graph and we have no indication that performance is limited.
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