| 摘要: |
| 结合量子近似优化算法求解约束优化问题是当前的研究热点之一,针对约束优化问题,提出了一种在量子
近似优化算法框架中的改进方法;此方法融合了二次无约束二元优化和量子交替拟设这两种方法,同时将在目标
算符中添加惩罚项,将不符合解的期望值降低和通过对问题进行求解得出问题的可行解,将混合操作限定在可行
解空间内融合在一起;优点在于在求解约束优化问题时,能减小迭代次数,快速并准确地得到问题的最优解;以最
小顶点覆盖问题为例,将提出的方法与几种已有的方法做比较,得出方法能减小量子近似优化算法的迭代次数,使
得能够高质量和高效率的求解约束优化问题。 |
| 关键词: 量子近似优化算法 最小顶点覆盖问题 惩罚项 可行解 |
| DOI： |
| 分类号: |
| 基金项目: |
|
|
|
LIU Chang， ZHANG Xuefeng
|
|
School of Computer Science and Technology， Anhui University of Technology， Anhui Maanshan 243000， China
|
| Abstract: |
| Combining quantum approximate optimization algorithm to solve the constrained optimization problem is one of
the current research hotspots. In order to solve the constrained optimization problem an improved method is proposed in
the framework of the quantum approximate optimization algorithm. This method combines the quadratic unconstrained
binary optimization method and the quantum alternate ansatz method adds penalty term to the target operator reduces the
expected value of nonconforming solution and obtains feasible solution by solving the problem and limits the mixing
operation to the feasible solution space and fuses together. The advantage of this method is that it can reduce the number
of iterations and get the optimal solution quickly and accurately when solving constrained optimization problems. Taking
the minimum vertex coverage problem as an example the proposed method is compared with several existing methods and
it is concluded that the proposed method can reduce the number of iterations of the quantum approximate optimization
algorithm so that the constrained optimization problem can be solved with high quality and high efficiency. |
| Key words: quantum approximate optimization algorithm minimum vertex coverage penalty term feasible solution |
