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Diamond-shaped quantum circuit for real-time quantum dynamics in one dimension

Physical Review Research 2024年第4期6卷 043318-043318页

作者： Shohei Miyakoshi Takanori Sugimoto Tomonori Shirakawa Seiji Yunoki Hiroshi Ueda Center for Quantum Information and Quantum Biology Osaka University Toyonaka Osaka 560-8531 Japan Computational Materials Science Research Team RIKEN Center for Computational Science (R-CCS) Kobe Hyogo 650-0047 Japan Advanced Science Research Center Japan Atomic Energy Agency Tokai Ibaraki 319-1195 Japan Quantum Computational Science Research Team RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan Computational Condensed Matter Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Wako Saitama 351-0198 Japan RIKEN Interdisciplinary Theoretical and Mathematical Science Program (iTHEMS) Wako Saitama 351-0198 Japan Computational Quantum Matter Research Team RIKEN Center for Emergent Matter Science (CEMS) Wako Saitama 351-0198 Japan

In recent years, quantum computing has evolved as an exciting frontier, with the development of numerous algorithms dedicated to constructing quantum circuits that adeptly represent quantum many-body states. However, this domain remains in its early stages and requires further refinement to better understand the effective construction of highly entangled quantum states within quantum circuits. Here, we demonstrate that quantum many-body states can be universally represented using a quantum circuit comprising multiqubit gates. Furthermore, we evaluate the efficiency of a quantum circuit constructed with two-qubit gates in quench dynamics for the transverse-field Ising model. In this specific model, despite the initial state being classical without entanglement, it undergoes long-time evolution, eventually leading to a highly entangled quantum state. Our results reveal that a diamond-shaped quantum circuit, designed to approximate the multiqubit gate-based quantum circuit, remarkably excels in accurately representing the long-time dynamics of the system. Moreover, the diamond-shaped circuit follows the volume law behavior in entanglement entropy, offering a significant advantage over alternative quantum circuit constructions employing two-qubit gates.

关键词： Quantum entanglement

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Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging

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aBIOTECH 2024年第3期5卷 281-297页

作者： Sishi Chen Xuqi Lu Hongda Fang Anand Babu Perumal Ruyue Li Lei Feng Mengcen Wang Yufei Liu College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China State Key Laboratory of Rice Biology and Breeding Zhejiang UniversityHangzhou 310058China Computational Modeling and Nanoscale Processing Unit National Institute of Food TechnologyEntrepreneurship and Management-ThanjavurMinistry of Food Processing IndustriesThanjavur 613005India College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Global Education Program for AgriScience Frontiers Graduate School of AgricultureHokkaido UniversitySapporo 060-8589Japan

Bakanae disease,caused by Fusarium fujikuroi,poses a significant threat to rice production and has been observed in most rice-growing *** disease symptoms caused by different pathogens may vary,including elongated and weak stems,slender and yellow leaves,and dwarfism,as *** disease is likely to cause necrosis of diseased seedlings,and it may cause a large area of infection in the field through the transmission of ***,early disease surveillance plays a crucial role in securing rice *** monitoring methods are both time-consuming and labor-intensive and cannot be broadly *** this study,a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae *** data were obtained on the 9th,15th,and 21st day after rice infection to explore the physiological and biochemical performance,which helps to deepen the research on the disease *** data were obtained over these same periods of infection,and a deep learning model,named Rice Bakanae Disease-Visual Geometry Group(RBD-VGG),was established by leveraging hyperspectral imaging technology and deep learning *** on this model,an average accuracy of 92.2%was achieved on the 21st day of *** also achieved an accuracy of 79.4%as early as the 9th *** characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field ***,the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease,thereby providing an efficient avenue for disease prevention and control.

关键词： Bakanae disease Hyperspectral imaging Deep learning Early surveillance Disease monitoring

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Transparent Single-Cell Set Classification with Kernel Mean Embeddings

arXiv

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arXiv 2022年

作者： Shan, Siyuan Baskaran, Vishal Athreya Yi, Haidong Ranek, Jolene Stanley, Natalie Oliva, Junier B. Department of Computer Science UNC Chapel Hill United States Computational Medicine Program Curriculum in Bioinformatics and Computational Biology UNC Chapel Hill United States Computational Medicine Program Department of Computer Science UNC Chapel Hill United States

Modern single-cell flow and mass cytometry technologies measure the expression of several proteins of the individual cells within a blood or tissue sample. Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample’s associated phenotype with machine learning models. Such a large set cardinality also limits the interpretability of machine learning models due to the difficulty in tracking how each individual cell influences the ultimate prediction. We propose using Kernel Mean Embedding to encode the cellular landscape of each profiled biological sample. Although our foremost goal is to make a more transparent model, we find that our method achieves comparable or better accuracies than the state-of-the-art gating-free methods through a simple linear classifier. As a result, our model contains few parameters but still performs similarly to deep learning models with millions of parameters. In contrast with deep learning approaches, the linearity and sub-selection step of our model makes it easy to interpret classification results. Analysis further shows that our method admits rich biological interpretability for linking cellular heterogeneity to clinical phenotype. © 2022, CC0.

关键词： Forecasting

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Distribution-based Sketching of Single-Cell Samples

arXiv

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arXiv 2022年

作者： Baskaran, Vishal Athreya Ranek, Jolene Shan, Siyuan Stanley, Natalie Oliva, Junier B. Department of Computer Science UNC-Chapel Hill United States Computational Medicine Program Curriculum in Bioinformatics and Computational Biology UNC-Chapel Hill United States Computational Medicine Program Department of Computer Science UNC-Chapel Hill United States

Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across the millions of cells in a multi-patient cohort. While bioinformatics approaches can be used to link immune cell heterogeneity to external variables of interest, such as, clinical outcome or experimental label, they often struggle to accommodate such a large number of profiled cells. To ease this computational burden, a limited number of cells are typically sketched or subsampled from each patient. However, existing sketching approaches fail to adequately subsample rare cells from rare cell-populations, or fail to preserve the true frequencies of particular immune cell-types. Here, we propose a novel sketching approach based on Kernel Herding that selects a limited subsample of all cells while preserving the underlying frequencies of immune cell-types. We tested our approach on three flow and mass cytometry datasets and on one single-cell RNA sequencing dataset and demonstrate that the sketched cells (1) more accurately represent the overall cellular landscape and (2) facilitate increased performance in downstream analysis tasks, such as classifying patients according to their clinical outcome. An implementation of sketching with Kernel Herding is publicly available at https://***/vishalathreya/Set-Summarization. © 2022, CC0.

关键词： Bioinformatics

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Modeling the dynamics of firms' technological impact

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Chinese Physics B 2021年第12期30卷 111-120页

作者： Shuqi Xu Manuel Sebastian Mariani Linyuan Lü Yangtze Delta Region Institute(Huzhou)&Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaHuzhou 313001China University Research Priority Program on Social Networks University of ZurichCH-8050 ZurichSwitzerland Beijing Computational Science Research Center Beijing 100193China

Recent studies in complexity science have uncovered temporal regularities in the dynamics of impact along scientific and other creative careers, but they did not extend the obtained insights to firms. In this paper, we show that firms' technological impact patterns cannot be captured by the state-of-the-art dynamical models for the evolution of scientists' research impact, such as the Q model. Therefore, we propose a time-varying returns model which integrates the empiricallyobserved relation between patent order and technological impact into the Q model. The proposed model can reproduce the timing pattern of firms' highest-impact patents accurately. Our results shed light on modeling the differences behind the impact dynamics of researchers and firms.

关键词： firm technological impact patent analysis impact dynamics

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Unbiased and Multilevel Methods for a Class of Diffusions Partially Observed via Marked Point Processes

arXiv

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arXiv 2023年

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider the filtering problem associated to partially observed diffusions, with observations following a marked point process. In the model, the data form a point process with observation times that have its intensity driven by a diffusion, with the associated marks also depending upon the diffusion process. We assume that one must resort to time-discretizing the diffusion process and develop particle and multilevel particle filters to recursively approximate the filter. In particular, we prove that our multilevel particle filter can achieve a mean square error (MSE) of O(∈2) (∈ > 0 and arbitrary) with a cost of O(∈-2.5) versus using a particle filter which has a cost of O(∈-3) to achieve the same MSE. We then show how this methodology can be extended to give unbiased (that is with no time-discretization error) estimators of the filter, which are proved to have finite variance and with high-probability have finite cost. Finally, we extend our methodology to the problem of online static-parameter estimation. Key words: Unbiased Methods, Multilevel Monte Carlo, Non-Linear Filtering, Point Processes, Parameter *** Codes 60G55, 60G35, 62M20, 62F30 © 2023, CC BY.

关键词： Diffusion

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Density functional theory investigation of electronic structure and muon hyperfine interaction in isolated adenine and thymine

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Interactions 2024年第1期245卷 1-14页

作者： Zaharim, Wan Nurfadhilah Sulaiman, Shukri Jamaludin, Ammaina Rozak, Harison Watanabe, Isao Department of Chemical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Nuclear Structure Research Group RIKEN Nishina Center for Accelerator Based Science 2-1 Hirosawa Wako Saitama351-0198 Japan Physics Section School of Distance Education Universiti Sains Malaysia Pulau Pinang Minden11800 Malaysia Computational Physics Laboratory School of Distance Education Universiti Sains Malaysia Pulau Pinang Minden11800 Malaysia Innovative Global Program College of Engineering Shibaura Institute of Technology Saitama337-8570 Japan

In DFT calculation, a basis set is an important factor in determining the quality of results, especially for hyperfine interaction where changes in the local geometry and electronic structure can have a big impact. The use of extensive basis sets is always desirable and is expected to produce better results. However, for DNA molecules of more than 10 base-pairs, all-electron geometry optimization calculation using large basis sets such as 6-311++G(d,p) is prohibitively difficult even on a supercomputer. An alternative is to use smaller basis sets such as 6-31G which is used in this work. Thus, the objective of the work reported here is to determine whether 6-31G basis set is suitable to be used to study muon hyperfine interaction in short DNA molecule. It is found that in comparison with 6-311++G(d,p), 6-31G does not change the ranking of muon sites stability in isolated adenine and thymine. Furthermore, the 6-31G calculated muon HFCCs are slightly closer to the experimental values. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Electronic structure

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Bayesian Parameter Inference for Partially Observed Stochastic Volterra Equations

arXiv

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arXiv 2023年

作者： Jasra, Ajay Ruzayqat, Hamza Wu, Amin Applied Mathematics & Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field they can be used to represent long memory in unobserved volatility processes. In many cases of practical interest, SVEs must be time-discretized and then parameter inference is based upon the posterior associated to this time-discretized process. Based upon recent studies on time-discretization of SVEs (e.g. [24]) we use Euler-Maruyama methods for the afore-mentioned discretization. We then show how multilevel Markov chain Monte Carlo (MCMC) methods [19] can be applied in this context. In the examples we study, we give a proof that shows that the cost to achieve a 4 mean square error (MSE) of O(ϵ2), ϵ > 0, is O(ϵ− 4/2H+1 ), where H is the Hurst parameter. If one uses a single level MCMC method then the cost is O(ϵ−2(2H+3)/ 2H+1 ) to achieve the same MSE. We illustrate these results in the context of state-space and stochastic volatility models, with the latter applied to real *** Codes 62F15, 62M20, 60J10, 60J22, 65C40 © 2023, CC BY.

关键词： Mean square error

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Electronic properties of 3d transition metal dihalide monolayers predicted by DFT methods: Is there a pattern or are the results random?

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Journal of the Chinese Chemical Society 2023年第3期70卷 359-371页

作者： Chiu, Cheng-chau Wang, Chung-Yu Huang, Bo-Jie Kuo, Jer-Lai Department of Chemistry National Sun Yat-sen University Kaohsiung Taiwan Center for Theoretical and Computational Physics National Sun Yat-sen University Kaohsiung Taiwan Green Hydrogen Research Center National Sun Yat-sen University Kaohsiung Taiwan Institute of Atomic and Molecular Sciences Academia Sinica Taipei Taiwan Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan Department of Chemistry National Tsing Hua University Hsinchu Taiwan International Graduate Program of Molecular Science and Technology (NTU-MST) National Taiwan University Taipei Taiwan

We use periodic DFT calculations at LDA and PBE level to investigate 3d transition metal dihalide (TMDH) monolayers in H- and T-phase. By analyzing the phonon dispersion, we have obtained a rough overview which combinations may form stable structures. We have focused on identifying and explaining trends in the predicted electronic properties. Although their geometric structures are simple, the associated electronic and magnetic properties are not as easy to understand. At first glance, it seems that there is no clear trend, as even isovalence-electronic TMDH monolayers formed from the same metal but different halides can feature different magnetic moments. The identification of potential trends is further complicated by the fact that for a significant number of species, LDA results and PBE results predict different ground-state electronic structures. By rigorously analyzing the potential energy surfaces associated with different magnetic moments, we could show that the apparent inconsistencies can be easily understood as a result of the differences in the relative energy between electronic states of different magnetic moments. We further show that the trends in the band gaps can be easily rationalized by an electron counting rule based on simple symmetry arguments. © 2022 Chemical Society Located in Taipei and Wiley-VCH GmbH.

关键词： 2D materials DFT electronic structure monolayer spin transition metal dihalides

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