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Systematic Improvements in Transmon Qubit Coherence Enabled by Niobium Surface Encapsulation

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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

Randomized Compiling for Scalable Quantum computing on a Noisy Superconducting Quantum Processor

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Physical Review X 2021年第4期11卷 041039-041039页

作者： Akel Hashim Ravi K. Naik Alexis Morvan Jean-Loup Ville Bradley Mitchell John Mark Kreikebaum Marc Davis Ethan Smith Costin Iancu Kevin P. O’Brien Ian Hincks Joel J. Wallman Joseph Emerson Irfan Siddiqi Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Graduate Group in Applied Science and Technology University of California at Berkeley Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02142 USA Quantum Benchmark Inc. Kitchener Ontario N2H 5G5 Canada Keysight Technologies Canada Kanata Ontario K2K 2W5 Canada Institute for Quantum Computing and Department of Applied Mathematics University of Waterloo Waterloo Ontario N2L 3G1 Canada

The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error that has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.

关键词： Quantum algorithms Quantum benchmarking Quantum control Quantum information with solid state qubits

Leveraging randomized compiling for the quantum imaginary-time-evolution algorithm

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Physical Review Research 2022年第3期4卷 033140-033140页

作者： Jean-Loup Ville Alexis Morvan Akel Hashim Ravi K. Naik Marie Lu Bradley Mitchell John-Mark Kreikebaum Kevin P. O'Brien Joel J. Wallman Ian Hincks Joseph Emerson Ethan Smith Ed Younis Costin Iancu David I. Santiago Irfan Siddiqi Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Inst. for Quantum Computing and Dept. of Applied Mathematics University of Waterloo Waterloo Ontario Canada N2L 3G1 Quantum Benchmark Inc. Kitchener Ontario Canada N2H 5G5

Recent progress in noisy intermediate-scale quantum (NISQ) hardware shows that quantum devices may be able to tackle complex problems even without error correction. However, coherent errors due to the increased complexity of these devices is an outstanding issue. They can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like quantum imaginary time evolution are susceptible to these errors. This article presents the combination of both noise tailoring using randomized compiling and error mitigation with purification. We also show that cycle benchmarking gives an estimate of the reliability of the purification. We apply this method to the quantum imaginary time evolution of a transverse field Ising model and report an energy estimation error and a ground-state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.

关键词： Measurement-based quantum computing Quantum algorithms Quantum benchmarking Quantum computation Quantum control Quantum information processing Quantum simulation

B-Spline CNNS on lie groups

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arXiv 2019年

作者： Bekkers, Erik J. Department of Applied Mathematics and Computer Science Centre for Analysis and Scientific Computing Eindhoven University of Technology Eindhoven Netherlands

Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations in which data characteristics are effectively learned, geometric data-augmentations are made obsolete, and predictable behavior under geometric transformations (equivariance) is guaranteed via group theory. Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory). In this paper we lift these limitations and propose a modular framework for the design and implementation of G-CNNs for arbitrary Lie groups. In our approach the differential structure of Lie groups is used to expand convolution kernels in a generic basis of B-splines that is defined on the Lie algebra. This leads to a flexible framework that enables localized, atrous, and deformable convolutions in G-CNNs by means of respectively localized, sparse and non-uniform B-spline expansions. The impact and potential of our approach is studied on two benchmark datasets: Cancer detection in histopathology slides in which rotation equivariance plays a key role and facial landmark localization in which scale equivariance is important. In both cases, GCNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail. Copyright © 2019, The Authors. All rights reserved.

关键词： Lie groups

ICGM-FRAX: Iterative Cross Graph Matching for Hip Fracture Risk Assessment using Dual-energy X-ray Absorptiometry Images

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arXiv 2025年

作者： Zhao, Chen Shaik, Anjum Keyak, Joyce H. Lane, Nancy E. Deng, Jeffrey D. Su, Kuan-Jui Sha, Qiuying Shen, Hui Deng, Hong-Wen Zhou, Weihua Department of Computer Science Kennesaw State University 680 Arntson Dr MariettaGA30060 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Radiological Sciences Department of Biomedical Engineering Department of Mechanical and Aerospace Engineering University of California IrvineCA92697 United States Department of Internal Medicine Division of Rheumatology UC Davis Health SacramentoCA95817 United States Geisel School of Medicine Dartmouth College HanoverNH03755 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Mathematical Sciences Michigan Technological University HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States

Hip fractures represent a major health concern, particularly among the elderly, often leading decreased mobility and increased mortality. Early and accurate detection of at risk individuals is crucial for effective intervention. In this study, we propose Iterative Cross Graph Matching for Hip Fracture Risk Assessment (ICGM-FRAX), a novel approach for predicting hip fractures using Dual-energy X-ray Absorptiometry (DXA) images. ICGM-FRAX involves iteratively comparing a test (subject) graph with multiple template graphs representing the characteristics of hip fracture subjects to assess the similarity and accurately to predict hip fracture risk. These graphs are obtained as follows. The DXA images are separated into multiple regions of interest (RoIs), such as the femoral head, shaft, and lesser trochanter. Radiomic features are then calculated for each RoI, with the central coordinates used as nodes in a graph. The connectivity between nodes is established according to the Euclidean distance between these coordinates. This process transforms each DXA image into a graph, where each node represents a RoI, and edges derived by the centroids of RoIs capture the spatial relationships between them. If the test graph closely matches a set of template graphs representing subjects with incident hip fractures, it is classified as indicating high hip fracture risk. We evaluated our method using 547 subjects from the UK Biobank dataset, and experimental results show that ICGM-FRAX achieved a sensitivity of 0.9869, demonstrating high accuracy in predicting hip fractures. © 2025, CC BY.

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Surface states and related quantum interference in ab initio electron transport

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Physical Review Research 2021年第3期3卷 033017-033017页

作者： Dongzhe Li Jonas L. Bertelsen Nick Papior Alexander Smogunov Mads Brandbyge Department of Physics Technical University of Denmark DK-2800 Kongens Lyngby Denmark CEMES Université de Toulouse CNRS 29 rue Jeanne Marvig F-31055 Toulouse France Center for Nanostructured Graphene Technical University of Denmark DK-2800 Kongens Lyngby Denmark DTU Computing Center Department of Applied Mathematics and Computer Science Technical University of Denmark DK-2800 Kongens Lyngby Denmark SPEC CEA CNRS Université Paris-Saclay CEA Saclay F-91191 Gif-sur-Yvette France

Shockley surface states (SS) have attracted much attention due to their role in various physical phenomena occurring at surfaces. It is also clear from experiments that they can play an important role in electron transport. However, accurate incorporation of surface states in abinitio quantum transport simulations remains still an unresolved problem. Here we go beyond the state-of-the-art nonequilibrium Green's function formalism through the evaluation of the self-energy in real-space, enabling electron transport without using artificial periodic in-plane conditions. We demonstrate the method on three representative examples based on Au(111): a clean surface, a metallic nanocontact, and a single-molecule junction. We show that SS can contribute more than 30% of the electron transport near the Fermi energy. A significant and robust transmission drop is observed at the SS band edge due to quantum interference in both metallic and molecular junctions, in good agreement with experimental measurements. The origin of this interference phenomenon is attributed to the coupling between bulk and SS transport channels and it is reproduced and understood by tight-binding model. Furthermore, our method predicts much better quantized conductance for metallic nanocontacts.

关键词： Ballistic transport Surface states Molecular junctions Density functional theory Nonequilibrium Green's function

Hybrid Migrating Birds Optimization Strategy for t-way Test Suite Generation

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： H L Zakaria K Z Zamli Fakhrud Din Faculty of Computing and Applied Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan 26600 Pekan Pahang Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Kampus Pauh Putra 02600 Arau Perlis Malaysia Department of Computer Science & IT University of Malakand KPK Pakistan

Hybrid meta-heuristics algorithms have gained popularity in recent years to solve t-way test suite generation problems due to better exploration and exploitation capabilities of the hybridization. This paper presents the implementation of meta-heuristic search algorithms that are Migrating Birds Optimization (MBO) algorithm and Genetic Algorithm (GA) hybrid to a t-way test data generation strategy. The proposed strategy is called Elitist Hybrid MBO-GA Strategy (EMBO-GA). Based on the published benchmarking results, the result of these strategies are competitive with most existing strategies in terms of the generated test size in many of the parameter configurations. In the case where this strategy is not the most optimal, the resulting test size is sufficiently competitive.

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UrbanBox: a Low Cost End-to-End Platform for Smart City Sensing

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UrbanBox: a Low Cost End-to-End Platform for Smart City Sens...

IEEE Southeastcon

作者： Xiaorui Liu Ali Sayghe Donglin Cai Christian Barth Charalambos Konstantinou Reza Arghandeh Department of Electrical and Computer Engineering Florida State University Department of Computer Science Florida State University Department of Computing Western Norway University of Applied Sciences

ISBN: (数字)9781728101378

ISBN: (纸本)9781728101385

This paper presents a low cost end-to-end and open-source urban sensor node, called UrbanBox, which is based on the concept of the Internet of Things (IoT). Through the sensing platform made by multiple UrbanBox nodes, users can collect, visualize, and store the real-time data from urban environments. The platform can serve as a tool for developing data-rich smart cities. This paper presents the design, data collection architecture, and the sensors validation process for the implemented prototype.

关键词： Temperature measurement Temperature sensors Monitoring Data visualization Smart cities Real-time systems

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

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arXiv 2023年

作者： Choudhary, Kamal Wines, Daniel Garrity, Kevin F. Williams, Maureen Tavazza, Francesca Li, Kangming Hattrick-Simpers, Jason Gupta, Vishu Romero, Aldo H. Krogel, Jaron T. Saritas, Kayahan Fuhr, Addis Ganesh, Panchapakesan Kent, Paul R.C. Yan, Keqiang Lin, Yuchao Ji, Shuiwang Blaiszik, Ben Reiser, Patrick Friederich, Pascal Agrawal, Ankit Tiwary, Pratyush Beyerle, Eric Minch, Peter Rhone, Trevor David Takeuchi, Ichiro Wexler, Robert B. Mannodi-Kanakkithodi, Arun Ertekin, Elif Mishra, Avanish Mathew, Nithin Wood, Mitchell Rohskopf, Andrew Dale Wang, Shih-Han Achenie, Luke E.K. Xin, Hongliang Biacchi, Adam J. Material Measurement Laboratory National Institute of Standards and Technology MD20899 United States Department of Materials Science and Engineering University of Toronto 27 King’s College Cir TorontoON Canada Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States Ludwig Institute for Cancer Research Princeton University PrincetonNJ08544 United States Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Nanophase Materials Science Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States Globus University of Chicago IL60637 United States Data Science Learning Division National Lab ArgonneIL60439 United States Institute of Nanotechnology Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Department of Materials Science and Engineering University of Maryland College ParkMD20742 United States Department of Chemistry Institute of Materials Science and Engineering Washington University in St. Louis St. LouisMO63130 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Department of Mechan

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://***/jarvis_leaderboard/ © 2023, CC BY.

关键词： Electronic structure