Evidence map›Paper›PMID 41924173›Full record

ArticleNature electronics2026

Fully autonomous tuning of a spin qubit.

Jonas Schuff, Miguel J Carballido, Madeleine Kotzagiannidis, Juan Carlos Calvo, Marco Caselli, Jacob Rawling, David L Craig, Barnaby van Straaten, Brandon Severin, Federico Fedele and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature electronics, 2026. 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. Quantifying Strain and Its Effect on Charge Transport in Ge/Si Core/Shell Nanowires.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. Article
  3. 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

17 authors.

Jonas SchuffDepartment of Materials, University of Oxford, Oxford, UK.ORCID 0000-0002-8880-2125
Miguel J CarballidoDepartment of Physics, University of Basel, Basel, Switzerland.ORCID 0000-0001-7385-8284
Madeleine KotzagiannidisMind Foundry, Oxford, UK.
Juan Carlos CalvoMind Foundry, Oxford, UK.
Marco CaselliMind Foundry, Oxford, UK.
Jacob RawlingMind Foundry, Oxford, UK.
David L CraigDepartment of Materials, University of Oxford, Oxford, UK.
Barnaby van StraatenDepartment of Materials, University of Oxford, Oxford, UK.
Brandon SeverinDepartment of Materials, University of Oxford, Oxford, UK.
Federico FedeleDepartment of Engineering Science, University of Oxford, Oxford, UK.ORCID 0000-0003-4466-5576
Simon SvabDepartment of Physics, University of Basel, Basel, Switzerland.ORCID 0009-0004-0545-0307
Pierre Chevalier KwonDepartment of Physics, University of Basel, Basel, Switzerland.
Rafael S EggliDepartment of Physics, University of Basel, Basel, Switzerland.ORCID 0009-0007-3483-8986
Taras PatlatiukDepartment of Physics, University of Basel, Basel, Switzerland.
Nathan KordaMind Foundry, Oxford, UK.
Dominik M ZumbühlDepartment of Physics, University of Basel, Basel, Switzerland.ORCID 0000-0001-5831-633X
Natalia AresDepartment of Engineering Science, University of Oxford, Oxford, UK.ORCID 0000-0003-2588-6322

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of large-scale semiconductor quantum circuits is limited by the difficulties involved in efficiently tuning and operating such circuits. Identifying optimal operating conditions for these qubits is, in particular, complex and involves the exploration of vast parameter spaces. Here we report the autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations. Our approach integrates deep learning, Bayesian optimization and computer vision techniques. We demonstrate this automation in a germanium-silicon core-shell nanowire device. To illustrate the potential of full automation, we characterize how the Rabi frequency and

Indexed as

Condensed-matter physicsElectronic devices

Identifiers

PMID41924173
PMCPMC13035472

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.