A basic question regarding quantum entangled states is whether one can be probabilistically converted to another through local operations and classical communication exclusively. While the answer for bipartite systems is known, we show that for tripartite systems, this question encodes some of the most challenging open problems in mathematics and computer science. In particular, we show that there is no easy general criterion to determine the feasibility, and in fact, the problem is NP hard. In addition, we find obtaining the most efficient algorithm for matrix multiplication to be precisely equivalent to determining the maximum rate to convert the Greenberger-Horne-Zeilinger state to a triangular distribution of three EPR states. Our results are based on connections between multipartite entanglement and tensor rank (also called Schmidt rank), a key concept in algebraic complexity theory.
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http://dx.doi.org/10.1103/PhysRevLett.101.140502 | DOI Listing |
Eur Spine J
January 2025
Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
Background: Intervertebral disc (IVD) degeneration is the main cause of neck pain. Although conventional magnetic resonance imaging can detect morphological changes in intervertebral disc degeneration, it cannot provide accurate and objective evaluations. Magnetic resonance diffusion tensor imaging (DTI) reflects the microstructural changes in tissues by describing the diffusion of water molecules.
View Article and Find Full Text PDFBrain Sci
December 2024
School of Management Science and Information Engineering, Hebei University of Economics and Businesses, Shijiazhuang 050061, China.
A multimodal brain age estimation model could provide enhanced insights into brain aging. However, effectively integrating multimodal neuroimaging data to enhance the accuracy of brain age estimation remains a challenging task. In this study, we developed an innovative data fusion technique employing a low-rank tensor fusion algorithm, tailored specifically for deep learning-based frameworks aimed at brain age estimation.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.
Introduction: Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest without considering the underlying fiber geometry.
Methods: We propose a novel macrostructure-informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia.
Stat Interface
February 2024
Department of Statistics, Texas A&M University, College Station TX 77843, USA.
The development of modern sequencing technologies provides great opportunities to measure gene expression of multiple tissues from different individuals. The three-way variation across genes, tissues, and individuals makes statistical inference a challenging task. In this paper, we propose a Bayesian multi-way clustering approach to cluster genes, tissues, and individuals simultaneously.
View Article and Find Full Text PDFEur J Neurol
January 2025
Department of Internal Medicine, Hospital Bichat-Claude Bernard, Assistance Publique Hôpitaux de Paris, Université Paris Cité, Paris, France.
Background: Susac syndrome (SuS) is a rare immune-mediated microangiopathy with potential disabling evolution. We aimed to analyze brain microstructural damage through diffusion tensor imaging (DTI) in SuS and determine its association with poor outcomes.
Method: CarESS study is a prospective multicenter national cohort study of patients with SuS.
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