Evidence map›Paper›PMID 39774687›Full record

ArticleNature communications2025

Towards large-scale quantum optimization solvers with few qubits.

Marco Sciorilli, Lucas Borges, Taylor L Patti, Diego García-Martín, Giancarlo Camilo, Anima Anandkumar, Leandro Aolita

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. The Quantum Optimization Benchmarking Library.Nature computational science · 2026
    Article
  4. 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

7 authors.

Marco Sciorilli *Quantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE. Marco.Sciorilli@tii.ae.ORCID http://orcid.org/0009-0008-8952-6055
Lucas Borges *Quantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE.
Taylor L PattiNVIDIA, Santa Clara, CA, USA.
Diego García-MartínQuantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE.
Giancarlo CamiloQuantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE.ORCID http://orcid.org/0000-0002-1992-2502
Anima AnandkumarDepartment of Computing + Mathematical Sciences (CMS), California Institute of Technology (Caltech), Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-6974-6797
Leandro AolitaQuantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantum computers hold the promise of more efficient combinatorial optimization solvers, which could be game-changing for a broad range of applications. However, a bottleneck for materializing such advantages is that, in order to challenge classical algorithms in practice, mainstream approaches require a number of qubits prohibitively large for near-term hardware. Here we introduce a variational solver for MaxCut problems over

Identifiers

PMID39774687
PMCPMC11707289

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.