Evidence map›Paper›PMID 29259153›Full record

ArticleNature communications2017

Application of optimal band-limited control protocols to quantum noise sensing.

V M Frey, S Mavadia, L M Norris, W de Ferranti, D Lucarelli, L Viola, M J Biercuk

Abstract read
In one paragraph

Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  5. Article
  6. Multi-level quantum noise spectroscopy.Nature communications · 2021
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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.

V M FreyARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia.
S MavadiaARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia.
L M NorrisDepartment of Physics and Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH, 03755, USA.
W de FerrantiARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia.
D LucarelliJohns Hopkins University, Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA.
L ViolaDepartment of Physics and Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH, 03755, USA.
M J BiercukARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia. michael.biercuk@sydney.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Essential to the functionality of qubit-based sensors are control protocols, which shape their response in frequency space. However, in common control routines out-of-band spectral leakage complicates interpretation of the sensor's signal. In this work, we leverage discrete prolate spheroidal sequences (a.k.a. Slepian sequences) to synthesize provably optimal narrowband controls ideally suited to spectral estimation of a qubit's noisy environment. Experiments with trapped ions demonstrate how spectral leakage may be reduced by orders of magnitude over conventional controls when a near resonant driving field is modulated by Slepians, and how the desired narrowband sensitivity may be tuned using concepts from RF engineering. We demonstrate that classical multitaper techniques for spectral analysis can be ported to the quantum domain and combined with Bayesian estimation tools to experimentally reconstruct complex noise spectra. We then deploy these techniques to identify previously immeasurable frequency-resolved amplitude noise in our qubit's microwave synthesis chain.

Identifiers

PMID29259153
PMCPMC5736660

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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.