Evidence map›Paper›PMID 42129211›Full record

ArticleNature communications2026

Multiparameter quantum-enhanced adaptive metrology with squeezed light.

Giorgio Minati, Enrico Urbani, Nicolò Spagnolo, Valeria Cimini, Fabio Sciarrino

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Giorgio MinatiDipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.ORCID http://orcid.org/0009-0000-4896-9097
Enrico UrbaniDipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.
Nicolò SpagnoloDipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.ORCID http://orcid.org/0000-0003-3471-2252
Valeria CiminiDipartimento di Fisica, Sapienza Università di Roma, Roma, Italy. valeria.cimini@uniroma1.it.ORCID http://orcid.org/0000-0001-8513-3766
Fabio SciarrinoDipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.ORCID http://orcid.org/0000-0003-1715-245X

Funding

Ministero dell'Istruzione, dell'Università e della Ricerca (Ministry of Education, University and Research) CUP:B81I18001170001Ministero dell'Istruzione, dell'Università e della Ricerca (Ministry of Education, University and Research) PE0000023-NQSTI (Spoke 4)Ministero dell'Istruzione, dell'Università e della Ricerca (Ministry of Education, University and Research) R20TRHTSPA
6 · The paper itself

Abstract

Squeezed light enables quantum-enhanced phase estimation, with crucial applications in both fundamental physics and emerging technologies. To fully exploit the advantage provided by this approach, estimation protocols must remain optimal across the entire parameter range and resilient to instabilities in the probe state. In this context, strategies that rely on pre-calibrated squeezing levels are vulnerable to degradation over time and become sub-optimal when experimental conditions fluctuate. Here, we develop an adaptive multiparameter estimation strategy for ab-initio phase estimation, achieving sub-shot noise limit precision in the full periodicity interval [0, π), without relying on prior knowledge of the squeezing parameter. Our approach employs real-time feedback to jointly estimate both the optical phase and the squeezing level, ensuring robustness against experimental drifts and calibration errors. This self-calibrating scheme establishes a reliable quantum-enhanced sensing framework, opening new routes for practical scenarios and scalable distributed sensor networks using squeezed light.

Identifiers

PMID42129211
PMCPMC13376632

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LicenceCC BY
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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.