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Quantum Walks–Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration

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arXiv 2025年

作者： Chang, Yen Jui Wang, Wei-Ting Liu, Chen-Yu Wang, Yun-Yuan Chang, Ching-Ray Master Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist Taoyuan City320314 Taiwan Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist Taoyuan City320314 Taiwan Department of Physics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Graduate Institute of Applied Physics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan NVIDIA AI Technology Center NVIDIA Corp Taipei610101 Taiwan

We present a novel Adaptive Distribution Generator (ADG) that leverages a quantum walks–based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks(DTQWs)—specifically, split-step quantum walks(SSQWs) and their entangled extensions—to dynamically tune coin parameters and drive the evolution of quantum states towards desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and structured two-dimensional pattern generation exemplified by digit representations (0–9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walk–Based Adaptive Distribution Generator (QWs-based ADG) achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation. © 2025, CC BY.

关键词： Qubits

Cosmology with Galaxy Cluster Properties using Machine Learning

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arXiv 2023年

作者： Qiu, Lanlan Napolitano, Nicola R. Borgani, Stefano Zhong, Fucheng Li, Xiaodong Radovich, Mario Lin, Weipeng Dolag, Klaus Tortora, Crescenzo Wang, Yang Remus, Rhea-Silvia Wu, Sirui Longo, Giuseppe School of Physics and Astronomy Sun Yat-sen University Zhuhai Campus 2 Daxue Road Tangjia Zhuhai519082 China CSST Science Center for Guangdong-Hong Kong-Macau Great Bay Area Zhuhai519082 China Department of Physics E. Pancini University Federico II Via Cinthia 6 Naples80126-I Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy INAF-Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 TriesteI-34143 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing INAF - Osservatorio Astronomico di Padova via dell’Osservatorio 5 Padova35122 Italy Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr.1 München81679 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Straße 1 Garching85741 Germany INAF – Osservatorio Astronomico di Capodimonte Salita Moiariello 16 Napoli80131 Italy Peng Cheng Laboratory No.2 Xingke 1st Street Shenzhen518000 China

Context. Galaxy clusters are the largest gravitating structures in the universe and their mass assembly is sensitive to the underlying cosmology. Their mass function, baryon fraction, and mass distribution have been used to infer cosmological parameters, despite the presence of systematics. However, the complexity of the scaling relations among galaxy cluster properties has never been fully exploited, limiting their potential as a cosmological probe. Aims. We propose the first Machine Learning (ML) method using galaxy cluster properties from hydrodynamical simulations in different cosmologies to predict cosmological parameters combining a series of canonical cluster observables, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, and velocity dispersion at different redshifts. Methods. The machine learning model is trained on mock "measurements" of these observable quantities from Magneticum multi-cosmology simulations to derive unbiased constraints on a set of cosmological parameters. These include the mass density parameter, Ωm, the power spectrum normalization, σ8, the baryonic density parameter, Ωb, and the reduced Hubble constant, h0. Results. We test the ML model on catalogs of a few hundred clusters taken, in turn, from each simulation and find that the ML model can correctly predict the cosmology they have been picked from. The cumulative accuracy depends on the cosmology, ranging from 21% to 75%. We demonstrate that this is sufficient to derive unbiased constraints on the main cosmological parameters with errors of the order of ∼ 14% for Ωm, ∼ 8% for σ8, ∼ 6% for Ωb, and ∼ 3% for h0. Conclusions. This proof-of-concept analysis, yet based on a limited variety of multi-cosmology simulations, shows that machine learning can efficiently map the correlations in the multi-dimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a

关键词： Cosmology

Correction: RPTOR blockade suppresses brain metastases of NSCLC by interfering the ceramide metabolism via hijacking YY1 binding

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Journal of experimental & clinical cancer research : CR 2024年第1期43卷 37页

作者： Ying Lin Yun Wu Qiangzu Zhang Xunwei Tu Sufang Chen Junfan Pan Nengluan Xu Ming Lin Peiwei She Gang Niu Yusheng Chen Hongru Li Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. Department of General Practice Medicine Fujian Provincial Hospital Fuzhou 350001 China. The High Performance Computing Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100095 China. The Centre for Experimental Research in Clinical Medicine Fujian Provincial Hospital Fuzhou 350001 Fujian China. The High Performance Computing Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100095 China. g.niu@***. Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. cysktz@***. Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. muzi131122@***. Fujian Provincial Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Shengli Clinical College of Fujian Medical University Fuzhou 350001 Fujian China. muzi131122@***.

KiDS-1000 and DES-Y1 combined: Cosmology from peak count statistics

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arXiv 2024年

作者： Harnois-Déraps, Joachim Heydenreich, Sven Giblin, Benjamin Martinet, Nicolas Tröster, Tilman Asgari, Marika Burger, Pierre Castro, Tiago Dolag, Klaus Heymans, Catherine Hildebrandt, Hendrik Joachimi, Benjamin Wright, Angus H. School of Mathematics Statistics and Physics Newcastle University Herschel Building Newcastle-upon-TyneNE1 7RU United Kingdom Department of Astronomy and Astrophysics University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States Institute for Astronomy University of Edinburgh Blackford Hill EdinburghEH9 3HJ United Kingdom Université d’Aix-Marseille CNRS CNES LAM Marseille France Institute for Particle Physics and Astrophysics ETH Zürich Zürich8092 Switzerland E.A Milne Centre University of Hull Cottingham Road HullHU6 7RX United Kingdom University of Hull Cottingham Road Kingston-upon-HullHU6 7RX United Kingdom Waterloo Centre for Astrophysics University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Argelander-Institut für Astronomie Auf dem Hügel 71 Bonn53121 Germany INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy INFN - Sezione di Trieste Via Valerio 2 TS Trieste34100 Italy IFPU - Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Max-Planck-Institut fur Astrophysik Karl-Schwarzschild Strasse 1 Garching85748 Germany German Centre for Cosmological Lensing Bochum44780 Germany Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

We analyse the fourth data release of the Kilo Degree Survey (KiDS-1000) and extract cosmological parameter constraints based on the cosmic shear peak count statistics. Peaks are identified in aperture mass maps in which the filter is maximally sensitive to angular scales in the range 2-4 arcmin, probing deep into the non-linear regime of structure formation. We interpret our results with a simulation-based inference pipeline, sampling over a broad wCDM prior volume and marginalising over uncertainties on shape calibration, photometric redshift distribution, intrinsic alignment and baryonic feedback. Our measurements constrain the structure growth parameter and the amplitude of the non-linear intrinsic alignment model to Σ8 ≡ σ8 [Ωm/0.3]0.60 = 0.765+−00030030 and AIA = 0.71+−004242, respectively, in agreement with previous KiDS-1000 results based on two-point shear statistics. These results are robust against modelling of the non-linear physics, different scale cuts and selections of tomographic bins. The posterior is also consistent with that from the Dark Energy Survey Year-1 peak count analysis presented in Harnois-Déraps et al (2021), and hence we jointly analyse both surveys with a common pipeline. We obtain Σjoint8 ≡ σ8 [Ωm/0.3]0.57 = 0.759+−00020017, in agreement with the Planck wCDM results. The shear-CMB tension on this parameter increases to 3.1σ when forcing w = −1.0, and to 4.1σ if comparing instead with S8joint,ΛCDM = 0.736+−00016018, one of the tightest constraints to date on this quantity. Residual biases in the photometric redshifts of the DES-Y1 data and in the modelling of small scales physics could lower this tension, however it is robust against other systematics. Limits in the accuracy of our emulator prevent us from constraining Ωm © 2024, CC BY.

关键词： Dark energy

Euclid's Sensitivity to Neutrino Parameters 12

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Euclid's Sensitivity to Neutrino Parameters

12th Neutrino Oscillation Workshop, NOW 2024

作者： Archidiacono, M. Lesgourgues, J. Casas, S. Pamuk, S. Schöneberg, N. Sakr, Z. Parimbelli, G. Schneider, A. Hervas Peters, F. Pace, F. Sabarish, V.M. Costanzi, M. Camera, S. Carbone, C. Clesse, S. Frusciante, N. Fumagalli, A. Monaco, P. Scott, D. Viel, M. Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN-Sezione di Milano Via Celoria 16 Milano20133 Italy RWTH Aachen University Aachen52056 Germany CSIC Univ. de Cantabria Avda. de los Castros s/n SantanderE-39005 Spain Martí i Franquès 1 Barcelona08028 Spain Institut für Theoretische Physik University of Heidelberg Philosophenweg 16 Heidelberg69120 Germany Université de Toulouse CNRS UPS CNES 14 Av. Edouard Belin Toulouse31400 France Université St Joseph Faculty of Sciences Beirut Lebanon Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Dipartimento di Fisica Università degli studi di Genova INFN-Sezione di Genova via Dodecaneso 33 Genova16146 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy Department of Astrophysics University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Dipartimento di Fisica Università degli Studi di Torino Via P. Giuria 1 Torino10125 Italy INFN-Sezione di Torino Via P. Giuria 1 Torino10125 Italy INAF-Osservatorio Astrofisico di Torino Via Osservatorio 20 TO Pino Torinese10025 Italy Hamburger Sternwarte University of Hamburg Gojenbergsweg 112 Hamburg21029 Germany Dipartimento di Fisica Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy Service de Physique Théorique CP225 Boulevard du Triophe Bruxelles1050 Belgium Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany INFN Sezione di Trieste Via Valerio 2 TS Tri

The European Space Agency launched its newest mission in July 2023: Euclid, which is designed to create the largest galaxy clustering and weak gravitational lensing survey to date. The complementarity of wide imaging and spectroscopy will provide excellent sensitivity to the history of structure formation, and hence to physics that affects that history. Here we present the latest forecasts of how Euclid's main cosmological probes will be able to constrain parameters from neutrino physics. Specifically we focus on the summed mass of neutrino species Í mv, as well as the effective number of additional relativistic species ΔNeff. We show how the forthcoming Euclid data should lead to unprecedented sensitivity for these parameters, and together with data from future cosmic microwave background experiments, could enable a detection of the neutrino mass scale. © Copyright owned by the author(s)

关键词： Cosmology

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

Strong Asymptotic Properties of Kernel Smoothing Estimation for NA Random Variables with Right Censoring

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arXiv 2019年

作者： Shi, Jian-Hua Xu, Jian-Sen Xu, Jin-Feng School of Mathematics and Statistics Minnan Normal University Zhangzhou363000 China Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China The Institute of Meteorological Big Data-Digital Fujian Zhangzhou363000 China Fujian Key Laboratory of Data Science and Statistics Zhangzhou363000 China

Most studies for negatively associated (NA) random variables consider the complete-data situation, which is actually a relatively ideal condition in practice. The paper relaxes this condition to the incomplete-data setting and considers kernel smoothing density and hazard function estimation in the presence of right censoring based on the Kaplan-Meier estimator. We establish the strong asymptotic properties for these two estimators to assess their asymptotic behavior and justify their practical use. Copyright © 2019, The Authors. All rights reserved.

关键词： Random variables

Comparative analysis of SIMILAR to RCD ONE(SRO) family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第4期 438页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture Faisalabad Sub-Campus Burewala Department of Chemistry and Biochemistry University of AgricultureFaisalabad Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad Department of Plant Breeding and Genetics University of AgricultureFaisalabad Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud Computing Centre for Advanced Studies in Agriculture and Food Security University of Agriculture Faisalabad Department of Plant Pathology University of California State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural Sciences Federal Seed Certification and Registration Department Ministry of National Food Security and Research School of Agriculture Sciences Zhengzhou University

Background SRO(Similar to RCD1) genes family is largely recognized for their importance in the growth,development,and in responding to environmental ***,genome-wide identification and functional characterization of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribution of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes during ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

关键词：

GMAN: A graph multi-attention network for traffic prediction

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arXiv 2019年

作者： Zheng, Chuanpan Fan, Xiaoliang Wang, Cheng Qi, Jianzhong Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University Xiamen China Digital Fujian Institute of Urban Traffic Big Data Research Xiamen University Xiamen China School of Informatics Xiamen University Xiamen China School of Computing and Information Systems University of Melbourne Melbourne Australia

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://***/zhengchuanpan/GMAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting