Quadratic Unconstrained Binary Optimization (QUBO) is a general-purpose modeling framework for combinatorial optimization problems and is a requirement for quantum annealers. This paper utilizes the eigenvalue decomposition of the underlying Q matrix to alter and improve the search process by extracting the information from dominant eigenvalues and eigenvectors to implicitly guide the search towards promising areas of the solution landscape. Computational results on benchmark datasets illustrate the efficacy of our routine demonstrating significant performance improvements on problems with dominant eigenvalues.

翻译：二次曲线不受限制的二进制优化(QUBO)是组合优化问题的一个通用模型框架,也是量子整流器的一项要求。本文利用基本Q矩阵的元值分解法改变和改进搜索过程,从主要的源值和源值中提取信息,暗含地指导寻找解决方案前景有希望的领域。基准数据集的计算结果说明了我们例行工作的效率,显示在主要源值问题上业绩的显著改善。