Evidence map›Paper›PMID 41330905›Full record

ReviewNature communications2025

Artificial intelligence for quantum computing.

Yuri Alexeev, Marwa H Farag, Taylor L Patti, Mark E Wolf, Natalia Ares, Alán Aspuru-Guzik, Simon C Benjamin, Zhenyu Cai, Shuxiang Cao, Christopher Chamberland and 18 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Global vaccine development.Nature medicine · 2026
    Review
  3. 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

28 authors.

Yuri AlexeevNVIDIA Corporation, Santa Clara, CA, USA.ORCID http://orcid.org/0000-0001-5066-2254
Marwa H FaragNVIDIA Corporation, Santa Clara, CA, USA.
Taylor L PattiNVIDIA Corporation, Santa Clara, CA, USA.
Mark E WolfNVIDIA Corporation, Santa Clara, CA, USA. mawolf@nvidia.com.ORCID http://orcid.org/0000-0003-0690-6789
Natalia AresDepartment of Engineering Science, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0003-2588-6322
Alán Aspuru-GuzikNVIDIA Corporation, Santa Clara, CA, USA.ORCID http://orcid.org/0000-0002-8277-4434
Simon C BenjaminQuantum Motion, London, United Kingdom.ORCID http://orcid.org/0000-0002-7766-5348
Zhenyu CaiDepartment of Engineering Science, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0001-5659-4301
Shuxiang CaoNVIDIA Corporation, Santa Clara, CA, USA.ORCID http://orcid.org/0000-0001-7178-4250
Christopher ChamberlandNVIDIA Corporation, Santa Clara, CA, USA.
Zohim ChandaniNVIDIA Corporation, Santa Clara, CA, USA.
Federico FedeleDepartment of Engineering Science, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0003-4466-5576
Ikko HamamuraNVIDIA Corporation, Santa Clara, CA, USA.ORCID http://orcid.org/0000-0001-6416-5515
Nicholas HarriganNVIDIA Corporation, Santa Clara, CA, USA.
Jin-Sung KimNVIDIA Corporation, Santa Clara, CA, USA.
Elica KyosevaNVIDIA Corporation, Santa Clara, CA, USA.ORCID http://orcid.org/0000-0002-9154-0293
Justin G LietzNVIDIA Corporation, Santa Clara, CA, USA.
Tom LuboweNVIDIA Corporation, Santa Clara, CA, USA.ORCID http://orcid.org/0009-0000-3325-7281
Alexander McCaskeyNVIDIA Corporation, Santa Clara, CA, USA.
Roger G MelkoDepartment of Physics and Astronomy, University of Waterloo, Waterloo, ON, Canada.
Kouhei NakajiNVIDIA Corporation, Santa Clara, CA, USA.
Alberto PeruzzoQubit Pharmaceuticals, Paris, France.
Pooja RaoNVIDIA Corporation, Santa Clara, CA, USA.
Bruno SchmittNVIDIA Corporation, Santa Clara, CA, USA.
Sam StanwyckNVIDIA Corporation, Santa Clara, CA, USA.
Norm M TubmanNASA Ames Research Center, California, USA.
Hanrui WangComputer Science Department, University of California Los Angeles, Los Angeles, CA, USA.
Timothy CostaNVIDIA Corporation, Santa Clara, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space.

Identifiers

PMID41330905
PMCPMC12673084

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.