Year: 2024
Type: article
Abstract: This research focuses on developing effective benchmarks for quadratic unconstrained binary optimization instances, crucial for evaluating the performance of Ising hardware and solvers. Currently, the field lacks accessible and reproducible models for systematically testing such systems, particularly in terms of detailed phase space characterization. Here, we introduce universal generative models based on an extension of Hebb’s rule of associative memory with asymmetric pattern weights. We conduct comprehensive calculations across different scales and dynamical equations, examining outcomes like the probabilities of reaching the ground state, planted state, spurious state, or other energy levels. Additionally, the generated problems reveal properties such as the easy-hard-easy complexity transition and complex solution cluster structures. This method offers a promising platform for analyzing and understanding the behavior of physical hardware and its simulations, contributing to future advancements in optimization technologies. The authors present a method for creating generative models that produce test instances for quadratic unconstrained binary optimization (QUBO) problems, inspired by Hebb’s rule. Their approach identifies key solution patterns and complexity transitions, providing a platform to evaluate the performance of Ising hardware and solvers efficiently.
Source: Communications Physics
Institutions: Weizmann Institute of Science, University of Cambridge
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,790
Award: G4903876956
OpenAlex ID: W4404518922