2019/03/31 by Itay Hen
Computer Science · Physics and Astronomy · #Benchmarking #Computability, Logic, AI Algorithms #Computer science #Imaging phantom #Ising model #Neural Networks and Applications #Physics #Quantum Computing Algorithms and Architecture #Statistical physics #cond-mat.stat-mech #quant-ph
paper · pdf · doi:10.1103/physrevapplied.12.011003
published as Phys. Rev. Applied 12, 011003 (2019) · 8 pages, 4 figures
openalex publication_date 2019/07/19 · arxiv created 2019/08/29 · arxiv updated 2019/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Recent years have witnessed the flourishing of experimental I\phantom\rule00exs\phantom\rule00exi\phantom\rule00exn\phantom\rule00exg m\phantom\rule00exa\phantom\rule00exc\phantom\rule00exh\phantom\rule00exi\phantom\rule00exn\phantom\rule00exe\phantom\rule00exs, special-purpose computational devices that promise to solve the world's toughest optimization problems in record times. Evaluating an Ising machine's performance is problematic, though, as it poses two seemingly contradictory requirements: On the one hand, the generated problem instances should be hard to solve, yet on the other hand they should have verifiable solutions. This study provides a methodology for generating random optimization-problem sets from linear systems of equations that possess both desired properties, thereby allowing direct, unbiased benchmarking of these physical optimization devices.