The dimension of a quantum state is traditionally seen as the number of superposed distinguishable states in a given basis. We propose an absolute, i.e., basis-independent, notion of dimensionality for ensembles of quantum states. It is based on whether a quantum ensemble can be simulated with states confined to arbitrary lower-dimensional subspaces and classical postprocessing. In order to determine the absolute dimension of quantum ensembles, we develop both analytical witness criteria and a semidefinite programming criterion based on the ensemble's information capacity. Furthermore, we construct explicit simulation models for arbitrary ensembles of pure quantum states subject to white noise, and in natural cases we prove their optimality. Also, efficient numerical methods are provided for simulating generic ensembles. Finally, we discuss the role of absolute dimensionality in high-dimensional quantum information processing.
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http://dx.doi.org/10.1103/PhysRevLett.133.240203 | DOI Listing |
J Phys Condens Matter
January 2025
University of Science and Technology Beijing, No. 30, Xueyuan Road, Haidian District, Beijing, 100083, CHINA.
Boron nitride (BN), renowned for its exceptional optoelectrical properties, mechanical robustness, and thermal stability, has emerged as a promising two-dimensional (2D) material. Reinforcing AZ80 magnesium alloy with BN can significantly enhance its mechanical properties. To investigate and predict this enhancement during hot deformation, we introduce two independent modeling approaches a modified Johnson-Cook (J-C) constitutive model and an Artificial Neural Network (ANN).
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Physics Department and NanoLund, Lund University, Box 118, 22100 Lund, Sweden.
The dimension of a quantum state is traditionally seen as the number of superposed distinguishable states in a given basis. We propose an absolute, i.e.
View Article and Find Full Text PDFInfect Drug Resist
December 2024
Department of Spine Surgery, The First People's Hospital of Kashi Prefecture, Kashi, Xinjiang, 844000, People's Republic of China.
Background: Tuberculous spondylitis (TS) and brucellar spondylitis (BS) exhibit certain similarities in clinical presentation and imaging characteristics, making differential diagnosis challenging. Developing a reliable differential diagnosis model can assist clinicians in distinguishing between these two conditions at an early stage, allowing for targeted prevention and treatment strategies.
Methods: Patients diagnosed with TS and BS were retrospectively collected and randomized into training and validation cohorts (ratio 7:3).
BMC Health Serv Res
January 2025
College of Health and Medicine, Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, TAS, Australia.
Objective: To evaluate the impact of absolute cardiovascular risk counselling on quality-of-life indices within a chest pain clinic.
Data Sources And Study Setting: Primary data was collected at the Royal Hobart Hospital, Australia, between 2014 and 2020.
Study Design: Patients attending an Australian chest pain clinic were randomised into a prospective, open-label, blinded-endpoint study over a minimum 12-months follow-up.
J Chem Inf Model
January 2025
Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions.
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