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Evidence for excitonic insulator ground state in triangulene Kagome lattice

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arXiv 2023年

作者： Delgado, Aidan Dusold, Carolin Jiang, Jingwei Cronin, Adam Louie, Steven G. Fischer, Felix R. Department of Chemistry University of California BerkeleyCA94720 United States Department of Physics University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kavli Energy NanoScience Institute The University of California Berkeley The Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet Division of Computing Data Science and Society University of California BerkeleyCA94720 United States

Electron-hole pair excitations in semiconductors have been predicted to be able to give rise to a highly correlated many-body ground state, the excitonic insulator (EI)1,2. Under appropriate conditions below a critical temperature (Tc), strongly bound electron-hole pairs spontaneously form and undergo a phase transition from a normal band insulator into an exciton condensate, transforming the parent material into a novel correlated insulator. Despite recent advances in spectroscopic tools, clear direct experimental evidence for the EI state has been elusive and is often obfuscated by accompanying electronic effects3-13. Here we present the reticular bottom-up synthesis of a Kagome lattice of [4]triangulene14, a two-dimensional (2D) covalent organic framework (COF) imbued with a deliberate excitonic instability15 — excitons with binding energies larger than the bandgap — arising from a pair of flat bands (FBs). Theoretical analyses based on first-principles calculations and scanning tunnelling spectroscopy (STS) reveal quasiparticle spectral signatures mixing valence (VB) and conduction (CB) characteristics of the FBs along with a non-trivial semiconducting gap that can only be explained by invoking many-body theory16,17. Our findings spectroscopically corroborate the nature of a FB induced exciton insulator ground state and provide a robust yet highly tuneable platform for the exploration of correlated quasi boson physics in quantum materials. Copyright © 2023, The Authors. All rights reserved.

关键词： Excitons

Quantum-parallel vectorized data encodings and computations on trapped-ions and transmons QPUs

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arXiv 2023年

作者： Balewski, Jan Amankwah, Mercy G. Van Beeumen, Roel Bethel, E. Wes Perciano, Talita Camps, Daan National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Mathematics Case Western Reserve University ClevelandOH United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States Computer Science Department San Francisco State University San FranciscoCA United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States

Compact quantum data representations are essential to the emerging field of quantum algorithms for data analysis. We introduce two new data encoding schemes, QCrank and QBArt, which have a high degree of quantum parallelism through uniformly controlled rotation gates. QCrank encodes a sequence of real-valued data as rotations of the data qubits, allowing for high storage density. QBArt directly embeds a binary representation of the data in the computational basis, requiring fewer quantum measurements and lending itself to well-understood arithmetic operations on binary data. We present several applications of the proposed encodings for different types of data. We demonstrate quantum algorithms for DNA pattern matching, Hamming weight calculation, complex value conjugation, and retrieving an O(400) bits image, all executed on the Quantinuum QPU. Finally, we use various cloud-accessible QPUs, including IBMQ and IonQ, to perform additional benchmarking experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital storage

Brain metastasis, EGFR mutation subtype and generation of EGFR-TKI jointly influence the treatment outcome of patient with EGFR-mutant NSCLC

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Scientific Reports 2023年第1期13卷 1-10页

作者： Ju, Jia-Shiuan Huang, Allen Chung-Cheng Tung, Pi-Hung Huang, Chi-Hsien Chiu, Tzu-Hsuan Wang, Chin-Chou Ko, How-Wen Chung, Fu-Tsai Hsu, Ping-Chih Fang, Yueh-Fu Guo, Yi-Ke Kuo, Chih-Hsi Scott Yang, Cheng-Ta Division of Thoracic Oncology Department of Thoracic Medicine College of Medicine Chang Gung Memorial Hospital Chang Gung University Taoyuan City Taiwan Thoracic Oncology Unit Chang Gung Memorial Hospital Cancer Center Taoyuan City Taiwan Division of Pulmonary & Critical Care Medicine Kaohsiung Chang Gung Memorial Hospital Kaohsiung Taiwan Data Science Institute Department of Computing Imperial College London London United Kingdom

Non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutation is brain metastasis (BM)-prone. We determined the impact of this hallmark, along with EGFR subtype and generation of tyrosine kinase inhibitor (TKI) treatment, on patients’ outcome. 553 metastatic EGFR-mutant NSCLC patients received front-line EGFR-TKI treatment. Progression-free survival (PFS), overall survival (OS) and secondary T790M rate were analysed. BM was observed in 211 (38.2%) patients. BM (HR 1.20 [95% CI 0.99–1.48];p = 0.053), ECOG PS 0–1 (HR 0.71 [95% CI 0.54–0.93];p = 0.014) and afatinib treatment (HR 0.81 [95% CI 0.66–0.99];p = 0.045) were associated with PFS. Afatinib-treated patients without BM demonstrated a significantly longer PFS (16.3 months) compared to afatinib-treated patients with BM (13.7 months) and to gefitinib/erlotinib-treated patients with (11.1 months) or without BM (14.2 months;p < 0.001). CNS-only progression trended higher in afatinib-treated patients. ECOG PS 0–1 (HR 0.41 [95% CI 0.31–0.56];p < 0.001) and EGFR L858R mutation (HR 1.46 [95% CI 1.13–1.88];p = 0.003), but not BM, were the predictors for OS. BM (OR 2.02 [95% CI 1.02–4.08];p = 0.040), afatinib treatment (OR 0.26 [95% CI 0.12–0.50];p < 0.001) and EGFR L858R mutation (OR 0.55 [95% CI 0.28–1.05];p = 0.070) were associated with secondary T790M rate. In BM patients, gefitinib/erlotinib-treated ones with 19 deletion mutation and afatinib-treated ones with L858R mutation had the highest and the lowest T790M rate (94.4% vs. 27.3%, p < 0.001), respectively. BM and generation of EGFR-TKI jointly impact PFS and secondary T790M rate in patients with EGFR-mutant NSCLC, whereas OS was mainly associated with EGFR subtype. © 2023, The Author(s).

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Variational secure cloud quantum computing

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Physical Review A 2022年第2期105卷 022603-022603页

作者： Yuta Shingu Yuki Takeuchi Suguru Endo Shiro Kawabata Shohei Watabe Tetsuro Nikuni Hideaki Hakoshima Yuichiro Matsuzaki Department of Physics Faculty of Science Division I Tokyo University of Science Shinjuku Tokyo 162-8601 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan NTT Communication Science Laboratories NTT Corporation3-1 Morinosato Wakamiya Atsugi Kanagawa 243-0198 Japan NTT Computer and Data Science Laboratories NTT corporation Musashino Tokyo 180-8585 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan

Variational quantum algorithms (VQAs) have been considered to be useful applications of noisy intermediate-scale quantum (NISQ) devices. Typically, in VQAs, a parametrized ansatz circuit is used to generate a trial wave function, and the parameters are optimized to minimize a cost function. On the other hand, blind quantum computing (BQC) has been studied in order to provide a quantum algorithm with security by using cloud networks. A client with a limited ability to perform quantum operations hopes to have access to a quantum computer of a server, and BQC allows the client to use the server's computer without leakage of the client's information (such as input, running quantum algorithms, and output) to the server. However, BQC is designed for fault-tolerant quantum computing, and this requires many ancillary qubits, which may not be suitable for NISQ devices. Here, we propose an efficient way to implement the NISQ computing with guaranteed security for the client. In our architecture, only N+1 qubits are required, under an assumption that the form of ansätze is known to the server, where N denotes the necessary number of the qubits in the original NISQ algorithms. The client only performs single-qubit measurements on an ancillary qubit sent from the server, and the measurement angles can specify the parameters for the ansätze of the NISQ algorithms. The no-signaling principle guarantees that neither parameters chosen by the client nor the outputs of the algorithm are leaked to the server. This work paves the way for new applications of NISQ devices.

关键词： Measurement-based quantum computing Quantum algorithms Quantum networks

Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI

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arXiv 2023年

作者： Gaggion, Nicolás Matheson, Benjamin A. Xia, Yan Bonazzola, Rodrigo Ravikumar, Nishant Taylor, Zeike A. Milone, Diego H. Frangi, Alejandro F. Ferrante, Enzo CONICET-UNL Santa Fe Argentina School of Computing University of Leeds Leeds United Kingdom Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering The University of Manchester Manchester United Kingdom Department of Cardiovascular Sciences KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom

Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. However, conventional surface mesh generation methods, such as active shape models and multi-atlas segmentation, are highly time-consuming and require complex processing pipelines to generate simulation-ready 3D meshes. In response, we introduce HybridVNet, a novel architecture for direct image-to-mesh extraction seamlessly integrating standard convolutional neural networks with graph convolutions, which we prove can efficiently handle surface and volumetric meshes by encoding them as graph structures. To further enhance accuracy, we propose a multiview HybridVNet architecture which processes both long axis and short axis CMR, showing that it can increase the performance of cardiac MR mesh generation. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation. Experiments on a comprehensive dataset from the UK Biobank confirm the potential of HybridVNet to significantly advance cardiac imaging and computational cardiology by efficiently generating high-fidelity and simulation ready meshes from CMR images. © 2023, CC BY-SA.

关键词： Convolutional neural networks

Resolvability of classical-quantum channels

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arXiv 2024年

作者： Hayashi, Masahito Cheng, Hao-Chung Gao, Li School of Data Science The Chinese University of Hong Kong Shenzhen Longgang District Shenzhen518172 China Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan School of Mathematics and Statistics Wuhan University Wuhan430072 China Department of Electrical Engineering Graduate Institute of Communication Engineering Department of Mathematics Institute of Applied Mathematical Sciences Center for Quantum Science and Engineering National Taiwan University Taipei10617 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Quantum Computing Center New Taipei City236 Taiwan

Channel resolvability concerns the minimum resolution for approximating the channel output. We study the resolvability of classical-quantum channels in two settings, for the channel output generated from the worst input, and form the fixed independent and identically distributed (i.i.d.) input. The direct part of the worst-input setting is derived from sequential hypothesis testing as it involves of non-i.i.d. inputs. The strong converse of the worst-input setting is obtained via the connection to identification codes. For the fixed-input setting, while the direct part follows from the known quantum soft covering result, we exploit the recent alternative quantum Sanov theorem to solve the strong converse. © 2024, CC BY.

关键词： Quantum channel

Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

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arXiv 2023年

作者： Acosta, Fernando Torales Mikuni, Vinicius Nachman, Benjamin Arratia, Miguel Karki, Bishnu Milton, Ryan Karande, Piyush Angerami, Aaron Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. © 2023, CC BY.

关键词： Calorimeters

Interpretable AI forecasting for numerical relativity waveforms of quasicircular, spinning, nonprecessing binary black hole mergers

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Physical Review D 2022年第2期105卷 024024-024024页

作者： Asad Khan E. A. Huerta Huihuo Zheng Department of Physics University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Data Science and Learning Division Argonne National Laboratory Lemont Illinois 60439 USA National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Computer Science University of Chicago Chicago Illinois 60637 USA Leadership Computing Facility Argonne National Laboratory Lemont Illinois 60439 USA

We present a deep-learning artificial intelligence model (AI) that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasicircular, spinning, nonprecessing binary black hole mergers. We used theNRHybSur3dq8 surrogate model to produce train, validation and test sets of ℓ=|m|=2 waveforms that cover the parameter space of binary black hole mergers with mass ratios q≤8 and individual spins |s{1,2}z|≤0.8. These waveforms cover the time range t∈[−5000 M,130 M], where t=0M marks the merger event, defined as the maximum value of the waveform amplitude. We harnessed the ThetaGPU supercomputer at the Argonne Leadership computing Facility to train our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, to fully train our model within 3.5 h. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms in the time range t∈[−100 M,130 M]. Sampling a test set of 190,000 waveforms, we find that the average overlap between target and predicted waveforms is ≳99% over the entire parameter space under consideration. We also combined scientific visualization and accelerated computing to identify what components of our model take in knowledge from the early and late-time waveform evolution to accurately forecast the latter part of numerical relativity waveforms. This work aims to accelerate the creation of scalable, computationally efficient and interpretable artificial intelligence models for gravitational wave astrophysics.

关键词： Gravitational waves Neuronal networks

A Lightweight Feature Selection Method Based on Rankability

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SSRN

SSRN 2024年

作者： Kong, Lingping Velasquez, Juan D. Perfilieva, Irina Pant, Millie Pan, Jeng-Shyang Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile Institute for Research and Applications of Fuzzy Modeling Centre of Excellence IT4Innovations division of The University of Ostrava Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India School of Artificial Intelligence Nanjing University of Information Science and Technology China Instituto Sistemas Complejos de Ingeniería Santiago Chile Santiago Chile Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków Poland Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India College of Computer Science and Engineering Shandong University of Science and Technology China

Within the realm of data mining and machine learning, feature selection stands out as an essential technique for dimensionality reduction, presenting both popularity and complexity. Feature selection involves a nuanced trade-off that results in information loss. This distinction underscores the necessity for the development of lightweight, efficient, and practical methods capable of minimizing information loss while executing dimensional reduction. In this paper, we introduce a rankability-based feature selection method. The concept of rankability, proposed in 2019 and similar to the entropy concept, remains relatively unexplored. Our proposed method distinguishes itself by its lightweight complexity, eliminating the need for iterative *** evaluate the effectiveness of our approach, we conducted experiments on forty-one datasets and compared the results with four other algorithms using refined data with 10\%, 30\%, and 50\% feature dimension reduction. The findings reveal that our rankability-based feature selection method consistently outperforms the other four methods across a majority of datasets in terms of accuracy, precision, recall, and F1-macro performance. Specifically, the classification score exceeds 0.9 on accuracy for 50\% feature dimension reduction in twelve datasets, with nine datasets achieving scores over 0.9 in recall, F1-macro, and precision. Overall, our proposed feature selection method demonstrates promising performance. © 2024, The Authors. All rights reserved.

关键词： Feature Selection