Real‐Time Nuclear Magnetic Resonance Detection Using Maximum Likelihood Estimation with Single‐Shallow‐Nitrogen‐Vacancy Centers in Quantum Heterodyne Measurements

Single‐nitrogen‐vacancy (NV) centers in diamond are highly promising quantum nuclear magnetic resonance (NMR) sensors. However, their exposure to many sources of noise, such as surface impurities, shot noise from avalanche photodiode overlaps, and spin‐state projection errors inherent in quantum systems, limits their usefulness. Often, long measurement durations are required to accumulate sufficient data for NMR signal detection via fast Fourier transform spectrometry. For practical reasons, methods to shorten the necessary accumulation time for NMR signal detection are greatly desired. In this article, an on‐line formulation of maximum likelihood estimation (MLE) signal processing for quantum heterodyne NMR measurements is presented as a step toward this goal. This MLE method reduces the required data accumulation time by orders of magnitude and provides good estimates of target frequency locales in real time. These results are significant to practitioners of NMR detection with single‐NV centers in diamond who require a quick litmus test for potential signals when probing a wide area.

Authors

Institutions

Publication Details

Journal
physica status solidi (a)
Published
2024-10-22
DOI
https://doi.org/10.1002/pssa.202400307
Citations
2
Primary Topic
Diamond and Carbon-based Materials Research
Type
article
Field-Weighted Citation Impact
0.23

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real‐Time Nuclear Magnetic Resonance Detection Using Maximum Likelihood Estimation with Single‐Shallow‐Nitrogen‐Vacancy Centers in Quantum Heterodyne Measurements

Tomoki Ota, Shinobu Onoda, Hiroshi Kawarada, Kazuki Otani et al.
2 citations
physica status solidi (a)
Diamond and Carbon-based Materials Research
0.23
article

Real‐Time Nuclear Magnetic Resonance Detection Using Maximum Likelihood Estimation with Single‐Shallow‐Nitrogen‐Vacancy Centers in Quantum Heterodyne Measurements

Tomoki Ota, Shinobu Onoda, Hiroshi Kawarada, Kazuki Otani, Yuto Miyake, Masato Tsugawa, Tokuyuki Teraji, Akirabha Chanuntranont, Yuki Ueda, Takashi Tanii, Takahiro Shinada, Shuntaro Usui, Daiki Saito
article en
2 citations

Abstract

Single‐nitrogen‐vacancy (NV) centers in diamond are highly promising quantum nuclear magnetic resonance (NMR) sensors. However, their exposure to many sources of noise, such as surface impurities, shot noise from avalanche photodiode overlaps, and spin‐state projection errors inherent in quantum systems, limits their usefulness. Often, long measurement durations are required to accumulate sufficient data for NMR signal detection via fast Fourier transform spectrometry. For practical reasons, methods to shorten the necessary accumulation time for NMR signal detection are greatly desired. In this article, an on‐line formulation of maximum likelihood estimation (MLE) signal processing for quantum heterodyne NMR measurements is presented as a step toward this goal. This MLE method reduces the required data accumulation time by orders of magnitude and provides good estimates of target frequency locales in real time. These results are significant to practitioners of NMR detection with single‐NV centers in diamond who require a quick litmus test for potential signals when probing a wide area.

physica status solidi (a)Vol. 222(5)
Waseda University (JP), Tohoku University (JP), National Institute for Materials Science (JP), National Institutes for Quantum Science and Technology (JP)
Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science
Openalex Percentile: Top 50%
Diamond and Carbon-based Materials Research
0.23
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.