Evidence map›Paper›PMID 42038524›Full record

ArticleNature reviews. Physics2026

Metrics for spin-based computing.

Hidekazu Kurebayashi, Giovanni Finocchio, Karin Everschor-Sitte, Jack C Gartside, Tomohiro Taniguchi, Artem Litvinenko, Akash Kumar, Johan Åkerman, Eleni Vasilaki, Kemal Selçuk and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature reviews. Physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

13 authors.

Hidekazu KurebayashiLondon Centre for Nanotechnology, University College London, 17-19 Gordon Street, London, WC1H 0AH, United Kingdom.
Giovanni FinocchioDepartment of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy.
Karin Everschor-SitteFaculty of Physics and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, 47057, Duisburg, Germany.
Jack C GartsideLondon Centre for Nanotechnology, Imperial College London, London, SW7 2AZ, United Kingdom.
Tomohiro TaniguchiNational Institute of Advanced Industrial Science and Technology (AIST), Research Center for Emerging Computing Technologies, Tsukuba, Ibaraki, 305-8568, Japan.
Artem LitvinenkoDepartment of Physics, University of Gothenburg, Gothenburg 41296, Sweden.
Akash KumarCenter for Science and Innovation in Spintronics (CSIS), Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577 Japan.
Johan ÅkermanCenter for Science and Innovation in Spintronics (CSIS), Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577 Japan.
Eleni VasilakiUniversity of Sheffield, Sheffield, S10 2TN, United Kingdom.
Kemal SelçukDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA.
Kerem Y ÇamsarıDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA.
Advait MadhavanNational Institute of Standards and Technology, Gaithersburg, MD, USA.
Shunsuke FukamiWPI Advanced Institute for Materials Research, Tohoku University, 2-1-1, Katahira, Sendai 980-8577, Japan.

Funding

Intramural NIST DOC 9999-NIST
6 · The paper itself

Abstract

Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by electrically manipulating the collective dynamics of the electron spin, that is inherently non-volatile, nonlinear and fast-operating, and can couple to other degrees of freedom such as photonic and phononic systems. This review explores key advances in integrating magnetic and spintronic elements into computational architectures, ranging from fundamental components like radio-frequency neurons/synapses and spintronic probabilistic-bits to broader frameworks such as reservoir computing and magnetic Ising machines. We discuss hardware-specific and task-dependent metrics to evaluate the computing performance of spin-based components and associate them with physical properties. Finally, we discuss challenges and future opportunities, highlighting the potential of spin-based computing in next-generation technologies.

Identifiers

PMID42038524
PMCPMC13108489

What OpenQuestion holds

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Registered trials

None linked

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.