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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tuğfe Ai, Bo The School of Electronic and Information Engineering The Frontiers Science Center for Smart Highspeed Railway System Beijing Jiaotong University Beijing100044 China Purple Mountain Laboratories Nanjing211111 China The Department of Computer Science KTH Royal Institute of Technology Kista164 40 Sweden The Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Leveraging hardware-control imperfections for error mitigation via generalized quantum subspace

arXiv

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arXiv 2023年

作者： Ohkura, Yasuhiro Endo, Suguru Satoh, Takahiko Van Meter, Rodney Yoshioka, Nobuyuki Graduate School of Media and Governance Keio University SFC Kanagawa Fujisawa252-0882 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan Keio University Quantum Computing Center Kanagawa Yokohama223-8522 Japan Graduate School of Science and Technology Keio University Kanagawa Yokohama223-8522 Japan Faculty of Environment and Information Studies Keio University SFC Kanagawa Fujisawa252-0882 Japan Department of Applied Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Wako-shi Saitama351-0198 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan

In the era of quantum computing without full fault-tolerance, it is essential to suppress noise effects via the quantum error mitigation techniques to enhance the computational power of the quantum devices. One of the most effective noise-agnostic error mitigation schemes is the generalized quantum subspace expansion (GSE) method, which unifies various mitigation algorithms under the framework of the quantum subspace expansion. Specifically, the fault-subspace method, a subclass of GSE method, constructs an error-mitigated quantum state with copies of quantum states with different noise levels. However, from the experimental aspect, it is nontrivial to determine how to reliably amplify the noise so that the error in the simulation result is efficiently suppressed. In this work, we explore the potential of the fault-subspace method by leveraging the hardware-oriented noise: intentional amplification of the decoherence, noise boost by insertion of identity, making use of crosstalk, and probabilistic implementation of noise channel. We demonstrate the validity of our proposals via both numerical simulations with the noise parameters reflecting those in quantum devices available via IBM Quantum, and also experiments performed therein. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

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Heterogeneous Entity Representation for Medicinal Synergy Prediction

arXiv

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arXiv 2024年

作者： Wu, Jiawei Wen, Jun Yan, Mingyuan Dong, Anqi Gao, Shuai Wang, Ren Chen, Can School of Medicine National University of Singapore 119077 Singapore Harvard Medical School Harvard University BostonMA02115 United States Division of Decision and Control Systems Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Harvey Cushing Neuro-Oncology Laboratories Department of Neurosurgery Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

Motivation: Forecasting the synergistic effects of drug combinations facilitates drug discovery and development, especially regarding cancer therapeutics. While numerous computational methods have emerged, most of them fall short in fully modeling the relationships among clinical entities including drugs, cell lines, and diseases, which hampers their ability to generalize to drug combinations involving unseen drugs. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. Results: We present a novel deep hypergraph learning method named Heterogeneous Entity Representation for MEdicinal Synergy prediction (HERMES) to predict the synergistic effects of anti-cancer drugs. Heterogeneous data sources, including drug chemical structures, gene expression profiles, and disease clinical semantics, are integrated into hypergraph neural networks equipped with a gated residual mechanism to enhance high-order relationship modeling. HERMES demonstrates state-of-the-art performance on two benchmark datasets, significantly outperforming existing methods in predicting the synergistic effects of drug combinations, particularly in cases involving unseen drugs. Availability: The source code is publicly available at https://***/Christina327/*** Codes 92C50, 05C65, 68T07 © 2024, CC BY.

关键词： Gene expression

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Quantifying fermionic nonlinearity of quantum circuits

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Physical Review Research 2022年第4期4卷 043100-043100页

作者： Shigeo Hakkaku Yuichiro Tashima Kosuke Mitarai Wataru Mizukami Keisuke Fujii Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation 3-9-11 Midoricho Musashino 180-8585 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan RIKEN Center for Quantum Computing (RQC) Hirosawa 2-1 Wako Saitama 351-0198 Japan Fujitsu Quantum Computing Joint Research Division at QIQB Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan

Variational quantum algorithms (VQAs) have been proposed as one of the most promising approaches to demonstrate quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, it has been unclear whether VQAs can maintain quantum advantage under the intrinsic noise of the NISQ devices, which deteriorates the quantumness. Here we propose a measure, called fermionic nonlinearity, to quantify the classical simulatability of quantum circuits designed for simulating fermionic Hamiltonians. Specifically, we construct a Monte Carlo type classical algorithm based on the classical simulatability of fermionic linear optics, whose sampling overhead is characterized by the fermionic nonlinearity. As a demonstration of these techniques, we calculate the upper bound of the fermionic nonlinearity of a rotation gate generated by four fermionic modes under the dephasing noise. Moreover, we estimate the sampling costs of the unitary coupled cluster singles and doubles quantum circuits for hydrogen chains subject to the dephasing noise. We find that, depending on the error probability and atomic spacing, there are regions where the fermionic nonlinearity becomes very small or unity, and hence the circuits are classically simulatable. We believe that our method and results help to design quantum circuits for fermionic systems with potential quantum advantages.

关键词： Quantum algorithms Quantum computation Quantum correlations in quantum information Quantum information processing Quantum simulation Resource theories

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Brute-force attack mitigation on remote access services via software-defined perimeter

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Scientific reports 2025年第1期15卷 18599页

作者： Francis A Ruambo Elijah E Masanga Beston Lufyagila Abdelhamied A Ateya Ahmed A Abd El-Latif May Almousa Bassem Abd-El-Atty School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China. College of Information Communication and Technology Mbeya University of Science and Technology (MUST) P.O Box 131 Mbeya Tanzania. Research and Development Department Aifrruis Laboratories Mbeya Tanzania. EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University 11586 Riyadh Saudi Arabia. Department of Electronics and Communications Engineering Zagazig University Zagazig 44519 Sharqia Egypt. EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University 11586 Riyadh Saudi Arabia. aabdellatif@psu.edu.sa. Center of Excellence in Quantum and Intelligent Computing Prince Sultan University 11586 Riyadh Saudi Arabia. aabdellatif@psu.edu.sa. Department of Mathematics and Computer Science Faculty of Science Menoufia University Shibin Al Kawm 32511 Egypt. aabdellatif@psu.edu.sa. Information Technology Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia. Department of Computer Science Faculty of Computers and Information Luxor University Luxor 85957 Egypt. bassem.abdelatty@fci.luxor.edu.eg.

Remote Access Services (RAS)-including protocols such as Remote Desktop Protocol (RDP), Secure Shell (SSH), Virtual Network Computing (VNC), Telnet, File Transfer Protocol (FTP), and Secure File Transfer Protocol (SFTP)-are essential to modern network infrastructures, particularly with the rise of remote work and cloud adoption. However, their exposure significantly increases the risk of brute-force attacks (BFA), where adversaries systematically guess credentials to gain unauthorized access. Traditional defenses like IP blocklisting and multifactor authentication (MFA) often struggle with scalability and adaptability to distributed attacks. This study introduces a zero-trust-aligned Software-Defined Perimeter (SDP) architecture that integrates Single Packet Authorization (SPA) for service cloaking and Connection Tracking (ConnTrack) for real-time session analysis. A Docker-based prototype was developed and tested, demonstrating no successful BFA attempts observed, latency reduction by above 10% across all evaluated RAS protocols, and the system CPU utilization reduction by 48.7% under attack conditions without impacting normal throughput. It also proved effective against connection-oriented attacks, including port scanning and distributed denial of service (DDoS) attacks. The proposed architecture offers a scalable and efficient security framework by embedding proactive defense at the authentication layer. This work advances zero-trust implementations and delivers practical, low-overhead protection for securing RAS against evolving cyber threats.

关键词： Brute-force attacks Remote access services (RAS) Single packet authorization (SPA) Software-defined perimeter (SDP) Zero-trust architecture

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Machine learning of noise-resilient quantum circuits

arXiv

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arXiv 2020年

作者： Cincio, Lukasz Rudinger, Kenneth Sarovar, Mohan Coles, Patrick J. Theoretical Division MS 213 Los Alamos National Laboratory Los AlamosNM87545 United States Quantum Computer Science Sandia National Laboratories AlbuquerqueNM87185 United States Extreme-Scale Data Science and Analytics Sandia National Laboratories LivermoreCA94550 United States

Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers. In this work, we present a general framework based on machine learning for reducing the impact of quantum hardware noise on quantum circuits. Our method, called noise-aware circuit learning (NACL), applies to circuits designed to compute a unitary transformation, prepare a set of quantum states, or estimate an observable of a many-qubit state. Given a task and a device model that captures information about the noise and connectivity of qubits in a device, NACL outputs an optimized circuit to accomplish this task in the presence of noise. It does so by minimizing a task-specific cost function over circuit depths and circuit structures. To demonstrate NACL, we construct circuits resilient to a fine-grained noise model derived from gate set tomography on a superconducting-circuit quantum device, for applications including quantum state overlap, quantum Fourier transform, and W-state preparation. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

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Generalized Quantum Subspace Expansion

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Physical Review Letters 2022年第2期129卷 020502-020502页

作者： Nobuyuki Yoshioka Hideaki Hakoshima Yuichiro Matsuzaki Yuuki Tokunaga Yasunari Suzuki Suguru Endo Department of Applied Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Wako-shi Saitama 351-0198 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 Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan

One of the major challenges for erroneous quantum computers is undoubtedly the control over the effect of noise. Considering the rapid growth of available quantum resources that are not fully fault tolerant, it is crucial to develop practical hardware-friendly quantum error mitigation (QEM) techniques to suppress unwanted errors. Here, we propose a novel generalized quantum subspace expansion method which can handle stochastic, coherent, and algorithmic errors in quantum computers. By fully exploiting the substantially extended subspace, we can efficiently mitigate the noise present in the spectra of a given Hamiltonian, without relying on any information of noise. The performance of our method is discussed under two highly practical setups: the quantum subspaces are mainly spanned by powers of the noisy state ρm and a set of error-boosted states, respectively. We numerically demonstrate in both situations that we can suppress errors by orders of magnitude, and show that our protocol inherits the advantages of previous error-agnostic QEM techniques as well as overcoming their drawbacks.

关键词： Quantum algorithms Quantum computation Quantum error correction Quantum information processing Variational approach

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Trilinos: Enabling Scientific Computing Across Diverse Hardware Architectures at Scale

arXiv

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arXiv 2025年

作者： Mayr, Matthias Heinlein, Alexander Glusa, Christian Rajamanickam, Sivasankaran Arnst, Maarten Bartlett, Roscoe A. Berger-Vergiat, Luc Boman, Erik G. Devine, Karen Harper, Graham Heroux, Michael Hoemmen, Mark Hu, Jonathan Kelley, Brian Kim, Kyungjoo Kouri, Drew P. Kuberry, Paul Liegeois, Kim Ober, Curtis C. Pawlowski, Roger Pearson, Carl Perego, Mauro Phipps, Eric Ridzal, Denis Roberts, Nathan V. Siefert, Christopher M. Thornquist, Heidi K. Tomasetti, Romin Trott, Christian R. Tuminaro, Raymond S. Willenbring, James M. Wolf, Michael M. Yamazaki, Ichitaro Data Science & Computing Lab Institute for Mathematics and Computer-Based Simulation Universität der Bundeswehr München Werner-Heisenberg-Weg 39 Neubiberg85577 Germany Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Sandia National Laboratories 1515 Eubank Blvd SE AlbuquerqueNM87123 United States University of Liege Quartier Polytech 1 allée de la Découverte 9 Liege4000 Belgium ParaTools Inc. 18125 Kreigle Lake Road AvonMN56310 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Advanced Micro Devices Inc. 2485 Augustine Dr Santa ClaraCA95054 United States

Trilinos is a community-developed, open-source software framework that facilitates building large-scale, complex, multiscale, multiphysics simulation code bases for scientific and engineering problems. Since the Trilinos framework has undergone substantial changes to support new applications and new hardware architectures, this document is an update to "An Overview of the Trilinos project" by Heroux et al. (ACM Transactions on Mathematical Software, 31(3):397–423, 2005). It describes the design of Trilinos, introduces its new organization in product areas, and highlights established and new features available in Trilinos. Particular focus is put on the modernized software stack based on the Kokkos ecosystem to deliver performance portability across heterogeneous hardware architectures. This paper also outlines the organization of the Trilinos community and the contribution model to help onboard interested users and *** Codes 65-04, 65Y05 Copyright © 2025, The Authors. All rights reserved.

关键词： Open source software

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Basketball player’s value evaluation by a networks-based variant parameter hidden markov model

arXiv

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arXiv 2020年

作者： Du, Xin Cai, Weihong Liu, Jianquan Yu, Ding Xu, Kai Li, Wei Department of Computer Science College of Engineering Shantou University China Big Data Research Institute Tong Xing Technology Corporation China System Platform Research Laboratories NEC Corporation Japan Graduate School of Science and Engineering Hosei University Japan

Determining the value of basketball players through analyzing the players’ behavior is important for the managers of modern basketball teams. However, conventional methods always utilize isolated statistical data, leading to ineffective and inaccurate evaluations. Existing models based on dynamic network theory offer major improvements to the results of such evaluations, but said models remain imprecise because they focus merely on evaluating the values of individual players rather than considering them within their current teams. To solve this problem, we propose an analysis and evaluation model based on networks and a hidden Markov model. To the best of our knowledge, we are the first to combine a network form representing the players who are playing with the use of a hidden Markov model to mine the network and generate the desired results. Applying our approach to SportVU data collected from the National Basketball Association shows that this analysis and evaluation model can effectively analyze the performance of each player in a game and provides an assistive tool for team managers. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Bigger is not always better: The importance of human-scale language modeling for psycholinguistics

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Journal of Memory and Language 2025年 144卷

作者： Ethan Gotlieb Wilcox Michael Y. Hu Aaron Mueller Alex Warstadt Leshem Choshen Chengxu Zhuang Adina Williams Ryan Cotterell Tal Linzen Department of Linguistics Georgetown University 1421 37th St NW Washington DC 20007 USA Department of Linguistics and Center for Data Science New York University 10 Washington Place NewYork NY 10003 USA Khoury College of Computer Sciences Northeastern University 440 Huntington Avenue Boston MA 02115 USA Department of Computer Science ETH Zürich Universitätstrasse 6 8092 Zürich Switzerland Electrical Engineering and Computer Science Department Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA FAIR Laboratories Meta Platforms Inc. 390 9th Ave NewYork NY 10001 USA

When trained to place high probability on a training corpus, neural network language models can learn a surprising amount about language. Recent work has demonstrated that large performance improvements can arise from simply increasing, i.e., scaling, the size of the corpora they are trained on and the number of parameters in those models. Accordingly, many contemporary systems are trained on trillions of words. While largely beneficial to performance on language applications, scaling has several downsides for both computational psycholinguistics and natural language processing research. We discuss the scientific challenges presented by the scaling paradigm, as well as the benefits that would result from language models that can learn from human-scale data. In the second half of this paper, we report on findings from a recent effort to bring about human-scale language model pretraining: the first iteration of the BabyLM Challenge, a shared task organized by the authors that invited participants to train a language model on 100 million words or less. The challenge produced several concrete best practices for practitioners interested in small-scale language modeling. For cognitive scientists, the challenge demonstrated that robust linguistic generalizations can be learned by models trained on a human-scale dataset, though this is not yet achieved through cognitively plausible mechanisms. Furthermore, it established a population of “BabyLMs” that are all effective at data-efficient language learning. Studying such models can help us identify hypotheses for the computational mechanisms that underlie human language acquisition.

关键词： Language modeling Scaling Cognitive modeling Connectionist networks Psycholinguistics Language acquisition

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