2016/08/20 by Walter Vinci, Daniel A. Lidar
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Benchmarking #Complexity and Algorithms in Graphs #Constraint Satisfaction and Optimization #Heuristic #Minification #Optimization problem #Point (geometry) #Quantum #Risk and Portfolio Optimization #math.OC #quant-ph
paper · pdf · doi:10.1103/physrevapplied.6.054016
published as Phys. Rev. Applied 6, 054016 (2016) · 21 pages, 8 figures
arxiv created 2016/08/20 · openalex created_date 2016/09/16 · openalex publication_date 2016/11/28 · arxiv updated 2016/12/07 · openalex updated_date 2026/08/06
Quantum computing may be the only pragmatic way to solve some problems, but when it is not absolutely necessary, is it actually worthwhile? The authors integrate the fields of heuristic optimization and optimal stopping to build a general framework for benchmarking randomized optimization algorithms. Their approach avoids bias and arbitrariness, and is particularly suited to determining the break-even point at which quantum optimization is superior to classical, when both raw performance and technology costs are taken into account.