Evidence map›Paper›PMID 41991551›Full record

ArticleNature communications2026

Higher-order neuromorphic Ising machines-autoencoders and Fowler-Nordheim annealers are all you need for scalability.

Faiek Ahsan, Saptarshi Maiti, Zihao Chen, Jakob Kaiser, Ankita Nandi, Madhuvanthi Srivatsav, Johannes Schemmel, Andreas G Andreou, Jason Eshraghian, Chetan Singh Thakur and 1 more

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Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Faiek AhsanDepartment of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.ORCID http://orcid.org/0009-0006-3981-998X
Saptarshi MaitiDepartment of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India.ORCID http://orcid.org/0000-0002-6557-8626
Zihao ChenDepartment of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.ORCID http://orcid.org/0009-0002-5945-5723
Jakob KaiserInstitute of Computer Engineering, Heidelberg University, Heidelberg, Germany.ORCID http://orcid.org/0000-0002-3586-2634
Ankita NandiDepartment of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India.
Madhuvanthi SrivatsavDepartment of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India.ORCID http://orcid.org/0009-0000-6913-9084
Johannes SchemmelInstitute of Computer Engineering, Heidelberg University, Heidelberg, Germany.
Andreas G AndreouDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-3826-600X
Jason EshraghianDepartment of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA.ORCID http://orcid.org/0000-0002-5832-4054
Chetan Singh ThakurDepartment of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India.ORCID http://orcid.org/0000-0002-1240-6214
Shantanu ChakrabarttyDepartment of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA. shantanu@wustl.edu.ORCID http://orcid.org/0000-0002-1688-6286

Funding

National Science Foundation (NSF) ECCS:2332166National Science Foundation (NSF) FET:2208770
6 · The paper itself

Abstract

We report that an autoencoder-based neuromorphic architecture, combined with Fowler-Nordheim annealing, is sufficient to implement scalable higher-order Ising machines. We show that these machines can consistently produce state-of-the-art solutions with high reliability and with competitive time-to-solution metrics. The autoencoder captures higher-order interactions by decomposing Ising clauses and Ising spins into encoder-decoder layers of spiking neurons, thereby keeping the resource complexity independent of the interaction order for sparse problems. An annealing process based on the dynamics of Fowler-Nordheim quantum mechanical tunneling extrapolates between an

Identifiers

PMID41991551
PMCPMC13270151

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.