2022/12/06 by Rathi Munukur, Bhaskar Roy Bardhan, Munukur, Rathi +5
Computer Science · Mathematics · #Advanced Optimization Algorithms Research #Complexity and Algorithms in Graphs #Computational Physics (physics.comp-ph) #FOS: Mathematics #FOS: Physical sciences #Optimization and Control (math.OC) #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2212.02669
openalex publication_date 2022/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.