论文标题
对置换组的量子启发的优化
Quantum-Inspired Optimization over Permutation Groups
论文作者
论文摘要
量子启发的优化（QIO）算法是计算技术，可模拟对经典硬件的某些量子机械效应，以解决一类优化任务。到目前为止，QIO方法已被用来解决各种二元优化问题，并且还报道了传统技术的显着（多项式）计算加速。在这项工作中，我们开发了一种称为Perm-Qio的算法框架，以量身定制Qio工具直接解决任意优化问题，其中在置换组上定义了基础成本函数的域。此类问题自然是二进制优化的自然而然的，因此不一定在直接实施传统Qio工具的范围内。我们证明了Perm-Qio在利用成本景观结构中找到高质量解决方案的功效，以解决一类属于置换空间的非平凡组合优化类别的车辆路由问题。
Quantum-inspired optimization (QIO) algorithms are computational techniques that emulate certain quantum mechanical effects on a classical hardware to tackle a class of optimization tasks. QIO methods have so far been employed to solve various binary optimization problems and a significant (polynomial) computational speedup over traditional techniques has also been reported. In this work, we develop an algorithmic framework, called Perm-QIO, to tailor QIO tools to directly solve an arbitrary optimization problem, where the domain of the underlying cost function is defined over a permutation group. Such problems are not naturally recastable to a binary optimization and, therefore, are not necessarily within the scope of direct implementation of traditional QIO tools. We demonstrate the efficacy of Perm-QIO in leveraging the structure of cost-landscape to find high-quality solutions for a class of vehicle routing problems that belong to the category of non-trivial combinatorial optimization over the space of permutations.
