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Precise measurement of magnetic fields is essential for various applications, such as fundamental physics, space exploration, and biophysics. Although recent progress in quantum engineering has assisted in creating advanced quantum magnetometers, there are still ongoing challenges in improving their efficiency and noise resistance. This study focuses on using symmetric graph state resources for quantum magnetometry to enhance measurement precision by analyzing the estimation theory under time-homogeneous and time-inhomogeneous noise models. The results show a significant improvement in estimating both single and multiple Larmor frequencies. In single Larmor frequency estimation, the quantum Fisher information spans a spectrum from the standard quantum limit to the Heisenberg limit within a periodic range of the Larmor frequency, and in the case of multiple Larmor frequencies, it can exceed the standard quantum limit for both noisy cases. This study highlights the potential of graph state-based methods for improving magnetic field measurements under noisy environments.
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http://dx.doi.org/10.1038/s41598-024-71365-8 | DOI Listing |
Circ Arrhythm Electrophysiol
September 2025
Department of Congenital Heart Disease, Evelina London Children's Hospital, United Kingdom (S. Chivers, T.V., V.Z., S.M., G.M., W.R., E.R., D.F.A.L., T.G.D., O.I.M., G.K.S., J.M.S.).
Background: Fetal tachycardias can cause adverse fetal outcomes including ventricular dysfunction, hydrops, and fetal demise. Postnatally, ECG is the gold standard, but, in fetal practice, echocardiography is used most frequently to diagnose and monitor fetal arrhythmias. Noninvasive extraction of the fetal ECG (fECG) may provide additional information about the electrophysiological mechanism and monitoring of intermittent arrhythmias.
View Article and Find Full Text PDFBiol Pharm Bull
September 2025
Computational and Biological Learning Laboratory, University of Cambridge, Cambridge CB21PZ, United Kingdom.
Neuroimaging in rodents holds promise for advancing our understanding of the central nervous system (CNS) mechanisms that underlie chronic pain. Employing two established, but pathophysiologically distinct rodent models of chronic pain, the aim of the present study was to characterize chronic pain-related functional changes with resting-state functional magnetic resonance imaging (fMRI). In Experiment 1, we report findings from Lewis rats 3 weeks after Complete Freund's adjuvant (CFA) injection into the knee joint (n = 16) compared with the controls (n = 14).
View Article and Find Full Text PDFJ Mol Graph Model
September 2025
Department of Physics, Patan Multiple Campus, Tribhuvan University, Patandhoka, Lalitpur, 44700, Bagmati, Nepal; Department of Physics, St. Xavier's College, Maitighar, Bagmati, 44600, Kathmandu, Nepal. Electronic address:
The bioactive organosulfur compound diallyl sulfide (DAS), found in garlic and onions, was analyzed using density functional theory (DFT). DAS exhibits antimicrobial and anticancer properties, making it a potential candidate for drug discovery. Geometry optimization revealed bond lengths and angles consistent with electron delocalization.
View Article and Find Full Text PDFComput Med Imaging Graph
August 2025
Academy for Engineering and Technology, Fudan University, Shanghai, 200433, People's Republic of China; Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, People's Republic of China; Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases,
Recent advancements in artificial intelligence have significantly enhanced the efficiency of abdominal MRI segmentation, thereby improving the screening and diagnosis of liver diseases. However, accurate precise liver segmentation in MRI remains a challenging task due to the high variability in liver morphology and the limited availability of high-quality annotated datasets. To address these challenges, this study presents an advanced semi-supervised learning framework that integrates cross-teaching with pseudo-label generation and intra-batch entropy minimization.
View Article and Find Full Text PDFJ Org Chem
September 2025
State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, P. R. China.
The Buchwald-Hartwig (B-H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B-H reaction by representing each reactant as an individual node within a custom-designed reaction graph, thereby capturing both single-molecule and intermolecular relationship features. Trained on a high-throughput B-H reaction data set, B-H Reaction Graph Neural Network (BH-RGNN) achieves near-state-of-the-art performance with an score of 0.971 while maintaining low computational costs.
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