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Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays

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Physical Review X 2025年第2期15卷 021034-021034页

作者： Anantha S. Rao Donovan Buterakos Barnaby van Straaten Valentin John Cécile X. Yu Stefan D. Oosterhout Lucas Stehouwer Giordano Scappucci Menno Veldhorst Francesco Borsoi Justyna P. Zwolak Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA National Institute of Standards and Technology Gaithersburg Maryland 20899 USA QuTech and Kavli Institute of Nanoscience Delft University of Technology P.O. Box 5046 2600 GA Delft The Netherlands QuTech and Netherlands Organisation for Applied Scientific Research (TNO) Delft The Netherlands Present address: NNF Quantum Computing Programme Niels Bohr Institute University of Copenhagen Blegdamsvej 17 2100 Copenhagen Denmark.

Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a modular automated virtualization system (MAViS)—a general and modular framework for autonomously constructing a complete stack of multilayer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.

关键词： Semiconductor quantum dots

A revised gomory-hu algorithm taking account of physical unavailability of network channels 27th

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A revised gomory-hu algorithm taking account of physical una...

27th International Conference on computer Networks, CN 2020

作者： Auzinger, Winfried Obelovska, Kvitoslava Stolyarchuk, Roksolyana Institute of Analysis and Scientific Computing TU Wien Vienna Austria Institute of Computer Science and Information Technologies Lviv Polytechnic National University Lviv Ukraine Institute of Applied Mathematics and Fundamental Sciences Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9783030507183

The classical Gomory-Hu algorithm aims for finding, for given input flows, a network topology for data transmission and bandwidth of its channels which are optimized subject to minimal bandwidth criteria. In practice, it may occur that some channels between nodes of the network are not active. Ignoring such channels using the topology obtained be the Gomory-Hu algorithm will not lead to an optimal flow-rate. In this paper the focus is on a modified algorithm taking into account deficient channels. While the classical algorithm generates a sequence of ring subnets, in our modified version the use of deficient channels is checked at intermediate stages in each cycle of the algorithm. When forming ring subnets, the availability of new channels to be introduced into the ring subnet is checked and in the case of unavailability another ring closest to the optimal one is selected. The network optimized by this modified algorithm guarantees the transmission of the maximum input stream. © Springer Nature Switzerland AG 2020.

关键词： Network topology

DUNE Software and High Performance computing

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arXiv 2022年

作者： Fleming, Bonnie Knoepfel, Kyle Lin, Meifeng Qian, Xin Ren, Yihui Viren, Brett Wei, Hanyu Yoo, Shinjae Yu, Haiwang Dept. of Physics Brookhaven National Laboratory United States Computer Science Brookhaven National Laboratory United States Dept. of Physics Yale University United States Dept. of Physics and Astronomy Louisiana State University United States Scientific Computing Division Fermi National Accelerator Laboratory United States

DUNE, like other HEP experiments, faces a challenge related to matching execution patterns of our production simulation and data processing software to the limitations imposed by modern high-performance computing facilities. In order to efficiently exploit these new architectures, particularly those with high CPU core counts and GPU accelerators, our existing software execution models require adaptation. In addition, the large size of individual units of raw data from the far detector modules pose an additional challenge somewhat unique to DUNE. Here we describe some of these problems and how we begin to solve them today with existing software frameworks and toolkits. We also describe ways we may leverage these existing software architectures to attack remaining problems going forward. This whitepaper is a contribution to the Computational Frontier of Snowmass21. © 2022, CC BY.

关键词： Data handling

Accelerating Communication for Parallel Programming Models on GPU Systems

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arXiv 2021年

作者： Choi, Jaemin Fink, Zane White, Sam Bhat, Nitin Richards, David F. Kale, Laxmikant V. Department of Computer Science University of Illinois Urbana-ChampaignIL United States Charmworks Inc. Illinois United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory CA United States

As an increasing number of leadership-class systems embrace GPU accelerators in the race towards exascale, efficient communication of GPU data is becoming one of the most critical components of high-performance computing. For developers of parallel programming models, implementing support for GPU-aware communication using native APIs for GPUs such as CUDA can be a daunting task as it requires considerable effort with little guarantee of performance. In this work, we demonstrate the capability of the Unified Communication X (UCX) framework to compose a GPU-aware communication layer that serves multiple parallel programming models of the Charm++ ecosystem: Charm++, Adaptive MPI (AMPI), and Charm4py. We demonstrate the performance impact of our designs with microbenchmarks adapted from the OSU benchmark suite, obtaining improvements in latency of up to 10.1x in Charm++, 11.7x in AMPI, and 17.4x in Charm4py. We also observe increases in bandwidth of up to 10.1x in Charm++, 10x in AMPI, and 10.5x in Charm4py. We show the potential impact of our designs on real-world applications by evaluating a proxy application for the Jacobi iterative method, improving the communication performance by up to 12.4x in Charm++, 12.8x in AMPI, and 19.7x in Charm4py. Copyright © 2021, The Authors. All rights reserved.

关键词： Python

Foundation Models for Atomistic Simulation of Chemistry and Materials

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arXiv 2025年

作者： Yuan, Eric C.-Y. Liu, Yunsheng Chen, Junmin Zhong, Peichen Raja, Sanjeev Kreiman, Tobias Vargas, Santiago Xu, Wenbin Head-Gordon, Martin Yang, Chao Blau, Samuel M. Cheng, Bingqing Krishnapriyan, Aditi Head-Gordon, Teresa Kenneth S. Pitzer Theory Center Department of Chemistry United States Department of Chemical and Biomolecular Engineering United States Electrical Engineering and Computer Science United States Bakar Institute of Digital Materials for the Planet University of California BerkeleyCA94720 United States Chemical Sciences Division United States Applied Mathematics and Computational Research Division United States Energy Technologies Area United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pre-training strategies is possible for learned simulations of chemistry and materials. The scaling of large and diverse datasets and highly expressive architectures for chemical and materials sciences should result in a foundation model that is more efficient and broadly transferable, robust to out-of-distribution challenges, and easily fine-tuned to a variety of downstream observables, when compared to specific training from scratch on targeted applications in atomistic simulation. In this Perspective we aim to cover the rapidly advancing field of machine learned interatomic potentials (MLIP), and to illustrate a path to create chemistry and materials MLIP foundation models at larger scale. © 2025, CC BY-SA.

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Measuring and Comparing QoE of Hybrid VR Applications under Increased Network Load

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Measuring and Comparing QoE of Hybrid VR Applications under ...

Immersive Learning Research Network (iLRN), Conference of the

作者： Ioannis Doumanis Daphne Economou Lemonia Argyriou S School of Physical Sciences and Computing University of Central Lanchashire Preston UK School of Computer Science and Engineering University of Westminster London UK Research Associate at National Center for Scientific Research “Demokritos” Athens GR

The elements of presence and interaction have been connected with high-quality learning in online learning environments. This can be achieved by offering learning environments and material that can engage the learner throughout the learning experience, allowing them to connect with the content, find relevance, construct meaning and critical thinking. Hybrid VR applications offer great potential for engaging learners in environments that accurately capture the world, allowing them to make personal connections and find relevance. Coupled with a branching narrative, computer-generated imagery (CGI) and interactivity can lead to highly engaging learning experiences. Hybrid VR applications, though, are bandwidth-demanding that can impact the learning experience. This paper presents a study comparing the Quality of Experience (QoE) of a hybrid VR application streamed over the standard internet and POINT network under increased network load. The paper presents the project motivations, a pilot study and its output. It closes with conclusions about the effect of the QoE and presence in learning and future directions.

关键词： Streaming media Quality of experience Standards

AdjointBackMapV2: Precise Reconstruction of Arbitrary CNN Unit’s Activation via Adjoint Operators

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arXiv 2021年

作者： Wan, Qing Cheung, Siu Wun Choe, Yoonsuck School of Computer Science and Technology Zhejiang Gongshang University Zhejiang Province 310018 China Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States

Adjoint operators have been found to be effective in the exploration of CNN’s inner workings [1]. However, the previous no-bias assumption restricted its generalization. We overcome the restriction via embedding input images into an extended normed space that includes bias in all CNN layers as part of the extended space and propose an adjoint-operator-based algorithm that maps high-level weights back to the extended input space for reconstructing an effective hypersurface. Such hypersurface can be computed for an arbitrary unit in the CNN, and we prove that this reconstructed hypersurface, when multiplied by the original input (through an inner product), will precisely replicate the output value of each unit. We show experimental results based on the CIFAR-10 and CIFAR-100 data sets where the proposed approach achieves near 0 activation value reconstruction error. Copyright © 2021, The Authors. All rights reserved.

关键词： Chemical activation

A Discretization Algorithm for k-Means with Capacity Constraints 6th

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A Discretization Algorithm for k-Means with Capacity Constra...

6th World Congress on Global Optimization, WCGO 2019

作者： Xu, Yicheng Xu, Dachuan Zhang, Dongmei Zhang, Yong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China

ISBN: (纸本)9783030218027

We consider capacitated k-means clustering whose object is to minimize the within-cluster sum of squared Euclidean distances. The task is to partition a set of n observations into k disjoint clusters satisfying the capacity constraints, both upper and lower bound capacities are considered. One of the reasons making these clustering problems hard to deal with is the continuous choices of the centroid. In this paper we propose a discretization algorithm that in polynomial time outputs an approximate centroid set with at most fractional loss of the original object. This result implies an FPT(k,d) PTAS for uniform capacitated k-means and makes more techniques, for example local search, possible to apply to it. © 2020, Springer Nature Switzerland AG.

关键词： Polynomial approximation

Learning Interface Conditions in Domain Decomposition Solvers

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arXiv 2022年

作者： Taghibakhshi, Ali Nytko, Nicolas Zaman, Tareq MacLachlan, Scott Olson, Luke West, Matthew Mechanical Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Computer Science University of Illinois Urbana-Champaign UrbanaIL61801 United States Scientific Computing Program Memorial University of Newfoundland and Labrador St. John'sNL Canada Mathematics and Statistics Memorial University of Newfoundland and Labrador St. John'sNL Canada

Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requires tedious analysis and is often available only in simplified, structured-grid settings, limiting their use for more complex problems. In this work, we generalize optimized Schwarz domain decomposition methods to unstructured-grid problems, using Graph Convolutional Neural Networks (GCNNs) and unsupervised learning to learn optimal modifications at subdomain interfaces. A key ingredient in our approach is an improved loss function, enabling effective training on relatively small problems, but robust performance on arbitrarily large problems, with computational cost linear in problem size. The performance of the learned linear solvers is compared with both classical and optimized domain decomposition algorithms, for both structured- and unstructured-grid problems. © 2022, CC BY.

关键词： Domain decomposition methods