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arXiv 2022年

作者： Dai, X. Trappen, R. Yang, R. Disseler, S.M. Basham, J.I. Gibson, J. Melville, A.J. Niedzielski, B.M. Das, R. Kim, D.K. Yoder, J.L. Weber, S.J. Hirjibehedin, C.F. Lidar, D.A. Lupascu, A. Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States Departments of Electrical & Computer Engineering Chemistry and Physics Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada

Flux tunability is an important engineering resource for superconducting circuits. Large-scale quantum computers based on flux-tunable superconducting circuits face the problem of flux crosstalk, which needs to be accurately calibrated to realize high-fidelity quantum operations. Typical calibration methods either assume that circuit elements can be effectively decoupled and simple models can be applied, or require a large amount of data. Such methods become ineffective as the system size increases and circuit interactions become stronger. Here we propose a new method for calibrating flux crosstalk, which is independent of the underlying circuit model. Using the fundamental property that superconducting circuits respond periodically to external fluxes, crosstalk calibration of N flux channels can be treated as N independent optimization problems, with the objective functions being the periodicity of a measured signal depending on the compensation parameters. We demonstrate this method on a small-scale quantum annealing circuit based on superconducting flux qubits, achieving comparable accuracy with previous methods. We also show that the objective function usually has a nearly convex landscape, allowing efficient optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Crosstalk

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Generating Approximate Ground States of Molecules Using Quantum Machine Learning

arXiv

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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

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FAIR for AI: An interdisciplinary and international community building perspective

arXiv

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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Multistage model for robust face alignment using deep neural networks

arXiv

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arXiv 2020年

作者： Wang, Huabin Cheng, Rui Zhou, Jian Tao, Liang Kwan, Hon Keung MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei Anhui230601 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

An ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. First, a spatial transformer - generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment. Then, stacked hourglass network is employed to obtain preliminary locations of landmarks as well as their corresponding scores. In addition, an exemplar-based shape dictionary is designed to determine landmarks with low scores based on those with high scores. By incorporating face shape constraints, misaligned landmarks caused by occlusions or cluttered backgrounds can be considerably improved. Extensive experiments based on challenging benchmark datasets are performed to demonstrate the superior performance of the proposed method over other state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Protein structured reservoir computing for spike-based pattern recognition

arXiv

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arXiv 2020年

作者： Tsakalos, Karolos-Alexandros Sirakoulis, Georgios Ch. Adamatzky, Andrew Smith, Jim Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Unconventional Computing Laboratory University of the West of England BristolBS16 1QY United Kingdom Computer Science Research Centre University of the West of England Xanthi Greece

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules - a technical goal undoubtedly demanding for novel devices. Following the trend, we explore an unconventional route of implementing reservoir computing on a single protein molecule and introduce neuromorphic connectivity with a small-world networking property. We have chosen Izhikevich spiking neurons as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a 'hardware' architecture of the communication networks connecting the processors. We apply on a single readout layer, various training methods in a supervised fashion to investigate whether the molecular structured Reservoir computing (RC) system is capable to deal with machine learning benchmarks. We start with the Remote Supervised Method, based on Spike-Timing-Dependent-Plasticity, and carry on with linear regression and scaled conjugate gradient back-propagation training methods. The RC network is evaluated as a proof-of-concept on the handwritten digit images from the standard MNIST and the extended MNIST datasets and demonstrates acceptable classification accuracies in comparison with other similar approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

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Quantum engineering with hybrid magnonics systems and materials

arXiv

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arXiv 2021年

作者： Awschalom, D.D. Du, C.H.R. He, R. Heremans, F.J. Hoffmann, A. Hou, J.T. Kurebayashi, H. Li, Y. Liu, L. Novosad, V. Sklenar, J. Sullivan, S.E. Sun, D. Tang, H. Tiberkevich, V. Trevillian, C. Tsen, A.W. Weiss, L.R. Zhang, W. Zhang, X. Zhao, L. Zollitsch, C.W. Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Materials Science Division Center for Molecular Engineering Argonne National Laboratory LemontIL United States Department of Physics University of California San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Materials Research Laboratory Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States London Centre for Nanotechnology University College London 17-19 Gordon Street LondonWCH1 0AH United Kingdom Materials Science Division Argonne National Laboratory LemontIL United States Department of Physics and Astronomy Wayne State University DetroitMI48201 United States Department of Physics North Carolina State University RaleighNC27695 United States Department of Electrical Engineering Yale University New HavenCT United States Department of Physics Oakland University MI48309 United States Institute for Quantum Computing Department of Chemistry University of Waterloo WaterlooON Canada Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Department of Physics University of Michigan Ann ArborMI48109 United States

Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing the different natural advantages of complementary quantum systems and for engineering new functionalities. This review focuses on the current frontiers with respect to utilizing magnetic excitatons or magnons for novel quantum functionality. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and flexibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant rich variety of physics and material selections enable exploration of novel quantum phenomena in materials science and engineering. In addition, the relative ease of generating strong coupling and forming hybrid dynamic systems with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we are focusing on the recent progress of magnon-magnon coupling within confined magnetic systems. Next we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

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Crosslink-net: Double-branch encoder segmentation network via fusing vertical and horizontal convolutions

arXiv

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arXiv 2021年

作者： Yu, Qian Qi, Lei Zhou, Luping Wang, Lei Yin, Yilong Shi, Yinghuan Wang, Wuzhang Gao, Yang School of Data and Computer Science Shandong Women's University Jinan250300 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Engineering Southeast University Nanjing211189 China The School of Electrical and Information Engineering University of Sydney NSW2006 Australia The School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia The School of Soft Shandong University Jinan250100 China Department of Respiratory and Critical Care Medicine Shandong Chest Hospital Jinan250013 China

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://***/Qianyu1226/*** Codes 68T07 Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)

arXiv

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arXiv 2023年

作者： Waldmannstetter, Diana Ezhov, Ivan Wiestler, Benedikt Campi, Francesco Kukuljan, Ivan Ehrlich, Stefan Vinayahalingam, Shankeeth Baheti, Bhakti Chakrabarty, Satrajit Baid, Ujjwal Bakas, Spyridon Schwarting, Julian Metz, Marie Kirschke, Jan S. Rueckert, Daniel Heckemann, Rolf A. Piraud, Marie Menze, Bjoern H. Kofler, Florian Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany Helmholtz AI Helmholtz Munich Neuherberg Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Healthcare Technologies SETLabs Research Munich Germany Department of Oral and Maxillofacial Surgery Radboud University Nijmegen Medical Centre Nijmegen Netherlands Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St.Louis St. LouisMO United States Artificial Intelligence in Healthcare and Medicine Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Medical Radiation Sciences University of Gothenburg Gothenburg Sweden

Accurate image registration is pivotal in bio-medical image analysis, where selecting suitable registration algorithms demands careful consideration. While numerous algorithms are available, the evaluation metrics to assess their performance have remained relatively static. This study addresses this challenge by introducing a novel evaluation metric termed Landmark Hit Rate (HitR), which focuses on the clinical relevance of image registration accuracy. Unlike traditional metrics such as Target Registration Error, which emphasize sub-resolution differences, HitR considers whether registration algorithms successfully position landmarks within defined confidence zones. This paradigm shift acknowledges the inherent annotation noise in medical images, allowing for more meaningful assessments. To equip HitR with label-noise-awareness, we propose defining these confidence zones based on an Inter-rater Variance analysis. Consequently, hit rate curves are computed for varying landmark zone sizes, enabling performance measurement for a task-specific level of accuracy. Our approach offers a more realistic and meaningful assessment of image registration algorithms, reflecting their suitability for clinical and biomedical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Systematic Improvements in Transmon Qubit Coherence Enabled by Niobium Surface Encapsulation

arXiv

引用

arXiv 2023年

作者： Bal, Mustafa Murthy, Akshay A. Zhu, Shaojiang Crisa, Francesco You, Xinyuan Huang, Ziwen Roy, Tanay Lee, Jaeyel van Zanten, David Pilipenko, Roman Nekrashevich, Ivan Lunin, Andrei Bafia, Daniel Krasnikova, Yulia Kopas, Cameron J. Lachman, Ella O. Miller, Duncan Mutus, Josh Y. Reagor, Matthew J. Cansizoglu, Hilal Marshall, Jayss Pappas, David P. Vu, Kim Yadavalli, Kameshwar Oh, Jin-Su Zhou, Lin Kramer, Matthew J. Lecocq, Florent Q. Goronzy, Dominic P. Torres-Castanedo, Carlos G. Pritchard, Graham Dravid, Vinayak P. Rondinelli, James M. Bedzyk, Michael J. Hersam, Mark C. Zasadzinski, John Koch, Jens Sauls, James A. Romanenko, Alexander Grassellino, Anna BataviaIL60510 United States Rigetti Computing BerkeleyCA94710 United States Ames Laboratory U.S. Department of Energy AmesIA50011 United States National Institute of Standards and Technology BoulderCO United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States The NUANCE Center Northwestern University EvanstonIL60208 United States International Institute of Nanotechnology Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Physics Illinois Institute of Technology ChicagoIL60616 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Hearne Institute of Theoretical Physics Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States

We present a novel transmon qubit fabrication technique that yields systematic improvements in T1 relaxation times. We fabricate devices using an encapsulation strategy that involves passivating the surface of niobium and thereby preventing the formation of its lossy surface oxide. By maintaining the same superconducting metal and only varying the surface structure, this comparative investigation examining different capping materials, such as tantalum, aluminum, titanium nitride, and gold, and film substrates across different qubit foundries definitively demonstrates the detrimental impact that niobium oxides have on the coherence times of superconducting qubits, compared to native oxides of tantalum, aluminum or titanium nitride. Our surface-encapsulated niobium qubit devices exhibit T1 relaxation times 2 to 5 times longer than baseline niobium qubit devices with native niobium oxides. When capping niobium with tantalum, we obtain median qubit lifetimes above 300 microseconds, with maximum values up to 600 microseconds, that represent the highest lifetimes to date for superconducting qubits prepared on both sapphire and silicon. Our comparative structural and chemical analysis suggests why amorphous niobium oxides may induce higher losses compared to other amorphous oxides. These results are in line with high-accuracy measurements of the niobium oxide loss tangent obtained with ultra-high Q superconducting radiofrequency (SRF) cavities. This new surface encapsulation strategy enables even further reduction of dielectric losses via passivation with ambient-stable materials, while preserving fabrication and scalable manufacturability thanks to the compatibility with silicon processes. © 2023, CC BY.

关键词： Fabrication

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Ultrathin active polarization-selective metasurface at X-band frequencies

引用

Physical Review B 2019年第7期100卷 075131-075131页

作者： Josip Lončar Anthony Grbic Silvio Hrabar Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 10000 Zagreb Croatia Radiation Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109-2122 USA

An ultrathin active polarization-selective metasurface is designed and optimized for operation in the X band. The metasurface leverages the design of a previously reported passive polarization converting metasurface. Similar to the passive polarization converter, the proposed active metasurface consists of three patterned metallic sheets. Its bottom sheet is modified and populated with ultrawideband, unconditionally stable amplifiers and corresponding biasing networks. The metasurface provides over 35 dB of input-output isolation achieved through orthogonal polarizations, which ensures stable operation. It was fabricated with a low-cost, printed-circuit-board and pick-and-place process. The performance of the metasurface was measured and its stability confirmed. The desired effects of amplification, polarization conversion, polarization selectivity, and nonreciprocal behavior were successfully demonstrated in both full-wave simulations and measurements. Due to its extremely small thickness (0.66 mm or 0.02λ at 10 GHz), the metasurface shows excellent performance for a wide range of incident angles, which exceeds ±30∘. It achieves a perfect (100%) polarization conversion ratio in simulation, and 98.4% in measurement.

关键词： Metamaterials Polarization

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