Higher-order neuromorphic Ising machines—autoencoders and Fowler-Nordheim annealers are all you need for scalability
annealing schedule. This not only ensures fast convergence towards high-quality solutions but also guarantees asymptotic convergence to the Ising ground state. To demonstrate the advantages of the proposed higher-order neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and MAX-SAT, comparing the results to those obtained using a second-order Ising machine employing the same annealing process.
Authors
- Johannes Schemmel (ORCID: https://orcid.org/0000-0003-1440-4375)
- Shantanu Chakrabartty (ORCID: https://orcid.org/0000-0002-1688-6286)
- Andreas G. Andreou (ORCID: https://orcid.org/0000-0003-3826-600X)
- Ankita Nandi (ORCID: https://orcid.org/0000-0003-4591-7675)
- Jason K. Eshraghian (ORCID: https://orcid.org/0000-0002-5832-4054)
- Chetan Singh Thakur
- Madhuvanthi Srivatsav
Institutions
- Johns Hopkins University (US)
- University of California, Santa Cruz (US)
- Washington University in St. Louis (US)
- Heidelberg University (DE)
- Indian Institute of Science Bangalore (IN)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-04-16
- DOI
- https://doi.org/10.1038/s41467-026-71937-4
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00