Benchmarking the optimization of optical machines with the planted solutions
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, pa...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-024-01870-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850162643956924416 |
|---|---|
| author | Nikita Stroev Natalia G. Berloff Nir Davidson |
| author_facet | Nikita Stroev Natalia G. Berloff Nir Davidson |
| author_sort | Nikita Stroev |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1a3c79168dc141009defcffb693de4aa |
| institution | OA Journals |
| issn | 2399-3650 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj-art-1a3c79168dc141009defcffb693de4aa2025-08-20T02:22:30ZengNature PortfolioCommunications Physics2399-36502024-11-01711910.1038/s42005-024-01870-9Benchmarking the optimization of optical machines with the planted solutionsNikita Stroev0Natalia G. Berloff1Nir Davidson2Department of Physics of Complex Systems, Weizmann Institute of ScienceDepartment of Applied Mathematics and Theoretical Physics, University of CambridgeDepartment of Physics of Complex Systems, Weizmann Institute of ScienceAbstract 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.https://doi.org/10.1038/s42005-024-01870-9 |
| spellingShingle | Nikita Stroev Natalia G. Berloff Nir Davidson Benchmarking the optimization of optical machines with the planted solutions Communications Physics |
| title | Benchmarking the optimization of optical machines with the planted solutions |
| title_full | Benchmarking the optimization of optical machines with the planted solutions |
| title_fullStr | Benchmarking the optimization of optical machines with the planted solutions |
| title_full_unstemmed | Benchmarking the optimization of optical machines with the planted solutions |
| title_short | Benchmarking the optimization of optical machines with the planted solutions |
| title_sort | benchmarking the optimization of optical machines with the planted solutions |
| url | https://doi.org/10.1038/s42005-024-01870-9 |
| work_keys_str_mv | AT nikitastroev benchmarkingtheoptimizationofopticalmachineswiththeplantedsolutions AT nataliagberloff benchmarkingtheoptimizationofopticalmachineswiththeplantedsolutions AT nirdavidson benchmarkingtheoptimizationofopticalmachineswiththeplantedsolutions |