Evidence map›Paper›PMID 40775523›Full record

ArticleScientific reports2025

Multi-level probabilistic computing: application to the multiway number partitioning problems.

Ki Hyuk Han, Gyuyoung Park, Jeong Ung Ahn, YongJin Kim, Deok Hyun Yun, Seung-Hun Jang, Young-Jun Nah, Min-Gu Kang, Hyun Cheol Koo, OukJae Lee

Abstract read
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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. NbOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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

10 authors.

Ki Hyuk HanKU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, South Korea.
Gyuyoung ParkCenter for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, South Korea.
Jeong Ung AhnKU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, South Korea.
YongJin KimCenter for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, South Korea.
Deok Hyun YunCenter for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, South Korea.
Seung-Hun JangCenter for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, South Korea.
Young-Jun NahCenter for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, South Korea.
Min-Gu KangCenter for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, South Korea.
Hyun Cheol KooKU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, South Korea. hckoo@kist.re.kr.
OukJae LeeCenter for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, South Korea. ojlee@kist.re.kr.

Funding

National Research Council of Science and Technology GTL24041-000
6 · The paper itself

Abstract

Probabilistic computing, a class of physics-based computing, bridges the gap between quantum computing and the classical von Neumann architecture. This approach provides more efficient means of addressing NP problems, which are challenging for classical computers. In this work, we analyze the core concept of probabilistic computing which is based on the Ising model framework-including bit fluctuations and energy trends. In addition, we extend the traditional binary (two-level) system into a multi-level probabilistic framework, i.e. number partitioning problem to multiway number partitioning problem, as a case study.

Indexed as

Combinatorial optimization problemMultiway number partitioningProbabilistic computing

Identifiers

PMID40775523
PMCPMC12331915

What OpenQuestion holds

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Registered trials

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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.