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arXiv 2012.04713quant-phcs.LG
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经典对称性与量子近似优化算法

Classical symmetries and the Quantum Approximate Optimization Algorithm

Argonne National Laboratory(阿贡国家实验室);NASA Ames Research Center(NASA艾姆斯研究中心);KBR(KBR公司);USRA Research Institute for Advanced Computer Science (RIACS)(美国大学空间研究协会高级计算机科学研究所);University of Delaware(特拉华大学)

Ruslan Shaydulin, Stuart Hadfield, Tad Hogg, Ilya Safro

摘要:

研究了量子近似优化算法(QAOA)与待优化目标函数的对称性之间的关系。我们的方法形式化的QAOA动力学和经典对称的目标函数组的量子对称性之间的联系。这种联系是一般性的,包括但不限于定义在图上的问题。我们展示了一系列的结果,探索连接和突出的例子,可以有效地获得一个非平凡的对称子群的硬问题类。特别是,我们展示了如何经典的目标函数对称性导致不变的测量结果的概率,通过这种对称性连接的状态,独立的算法参数或层数的选择。为了说明所开发的连接的力量,我们应用机器学习技术来预测基于对称性考虑的QAOA性能。我们提供的数值证据表明,一个小的图形对称属性足以预测最小QAOA深度所需的最大切割问题上实现目标近似比,在一个实际的重要设置QAOA参数时间表被约束为线性的,因此更容易优化。

英文摘要:

We study the relationship between the Quantum Approximate Optimization Algorithm (QAOA) and the underlying symmetries of the objective function to be optimized. Our approach formalizes the connection between quantum symmetry properties of the QAOA dynamics and the group of classical symmetries of the objective function. The connection is general and includes but is not limited to problems defined on graphs. We show a series of results exploring the connection and highlight examples of hard problem classes where a nontrivial symmetry subgroup can be obtained efficiently. In particular we show how classical objective function symmetries lead to invariant measurement outcome probabilities across states connected by such symmetries, independent of the choice of algorithm parameters or number of layers. To illustrate the power of the developed connection, we apply machine learning techniques towards predicting QAOA performance based on symmetry considerations. We provide numerical evidence that a small set of graph symmetry properties suffices to predict the minimum QAOA depth required to achieve a target approximation ratio on the MaxCut problem, in a practically important setting where QAOA parameter schedules are constrained to be linear and hence easier to optimize.

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。

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