论文标题
测试在量子退火器上对核心外围分配的QUBO公式
Testing a QUBO Formulation of Core-periphery Partitioning on a Quantum Annealer
论文作者
论文摘要
我们提出了一个新的内核，该内核可以量化成功为无向网络计算核心外围分区的任务。找到相关的最佳分区可以以二次无约束的二进制优化（QUBO）问题的形式表示，可以应用最先进的量子退火器。因此，我们利用新的目标函数来（a）判断量子退火器的性能，（b）将此方法与现有的启发式核心围栏分配方法进行比较。量子退火在市售的D-Wave机器上进行。即使基础网络稀疏，QUBO问题也涉及完整的矩阵。因此，我们开发和测试原始Qubo的稀疏版本，从而增加了量子退火器的可用问题维度。在合成和真实数据集上提供了结果，我们得出的结论是，QUBO/量子退火方法在优化这种新数量的兴趣方面提供了好处。
We propose a new kernel that quantifies success for the task of computing a core-periphery partition for an undirected network. Finding the associated optimal partitioning may be expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem, to which a state-of-the-art quantum annealer may be applied. We therefore make use of the new objective function to (a) judge the performance of a quantum annealer, and (b) compare this approach with existing heuristic core-periphery partitioning methods. The quantum annealing is performed on the commercially available D-Wave machine. The QUBO problem involves a full matrix even when the underlying network is sparse. Hence, we develop and test a sparsified version of the original QUBO which increases the available problem dimension for the quantum annealer. Results are provided on both synthetic and real data sets, and we conclude that the QUBO/quantum annealing approach offers benefits in terms of optimizing this new quantity of interest.
