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XACC: A System-Level Software Infrastructure for Heterogeneous Quantum-Classical computing∗

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arXiv 2019年

作者： McCaskey, Alexander J. Lyakh, Dmitry I. Dumitrescu, Eugene F. Powers, Sarah S. Humble, Travis S. Quantum Computing Institute Oak Ridge National Laboratory Oak RidgeTN37831 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Quantum programming techniques and software have advanced significantly over the past five years, with a majority focusing on high-level language frameworks targeting remote REST library APIs. As quantum computing architectures advance and become more widely available, lower-level, system software infrastructures will be needed to enable tighter, co-processor programming and access models. Here we present XACC, a system-level software infrastructure for quantum-classical computing that promotes a service-oriented architecture to expose interfaces for core quantum programming, compilation, and execution tasks. We detail XACC’s interfaces, their interactions, and its implementation as a hardware-agnostic framework for both near-term and future quantum-classical architectures. We provide concrete examples demonstrating the utility of this framework with paradigmatic tasks. Our approach lays the foundation for the development of compilers, associated runtimes, and low-level system tools tightly integrating quantum and classical workflows. Copyright © 2019, The Authors. All rights reserved.

关键词： Computer architecture

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

A Compact Scheme for Coupled Stochastic Nonlinear Schrodinger Equations

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Communications in computational Physics 2017年第1期21卷 93-125页

作者： Chuchu Chen Jialin Hong Lihai Ji Linghua Kong Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Institute of Applied Physics and ComputationalMathematics Beijing 100094China School of Mathematics and Information Science Jiangxi Normal UniversityNanchangJiangxi 330022China

In this paper,we propose a compact scheme to numerically study the coupled stochastic nonlinear Schrodinger *** prove that the compact scheme preserves the discrete stochastic multi-symplectic conservation law,discrete charge conservation law and discrete energy evolution law almost *** experiments confirm well the theoretical analysis ***,we present a detailed numerical investigation of the optical phenomena based on the compact *** numerical experiments for various amplitudes of noise,we find that the noise accelerates the oscillation of the soliton and leads to the decay of the solution amplitudes with respect to *** particular,if the noise is relatively strong,the soliton will be totally ***,we observe that the phase shift is sensibly modified by the ***,the numerical results present inelastic interaction which is different from the deterministic case.

关键词： Coupled stochastic nonlinear Schrodinger equations compact scheme stochastic multi-symplectic conservation law energy evolution law charge conservation law soliton evolution soliton interaction

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Julia for biologists

arXiv 2021年

作者： Roesch, Elisabeth Greener, Joe G. MacLean, Adam L. Nassar, Huda Rackauckas, Christopher Holy, Timothy E. Stumpf, Michael P.H. School of Mathematics and Statistics University of Melbourne 813 Swanston Street ParkvilleVIC3010 Australia Melbourne Integrative Genomics University of Melbourne 30 Royal Parade ParkvilleVIC3052 Australia Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Department of Quantitative and Computational Biology University of Southern California 1050 Childs Way Los AngelesCA90089 United States RelationalAI Inc. 2120 University Ave BerkeleyCA94794 United States Department of Mathematics Massachusetts Institute of Technology 182 Memorial Dr CambridgeMA02142 United States Julia Computing 240 Elm Street SomervilleMA02144 United States Pumas-AI 14711 Kamputa Drive CentervilleVA20120 United States Departments of Neuroscience and Biomedical Engineering Washington University in St. Louis 660 S. Euclid Ave. St. LouisMO63110 United States School of Bio-Sciences Biosciences 4 The University of Melbourne Royal Parade ParkvilleVIC3052 Australia

Increasing emphasis on data and quantitative methods in the biomedical sciences is making biological research more computational. Collecting, curating, processing, and analysing large genomic and imaging data sets poses major computational challenges, as does simulating larger and more realistic models in systems biology. Here we discuss how a relative newcomer among computer programming languages - Julia - is poised to meet the current and emerging demands in the computational biosciences, and beyond. Speed, flexibility, a thriving package ecosystem, and readability are major factors that make high-performance computing and data analysis available to an unprecedented degree to "gifted amateurs". We highlight how Julia's design is already enabling new ways of analysing biological data and systems, and we provide a, necessarily incomplete, list of resources that can facilitate the transition into the Julian way of computing. © 2021, CC BY.

关键词： Computer programming languages

Author Correction: Julia for biologists

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Nature methods 2023年第5期20卷 771页

作者： Elisabeth Roesch Joe G Greener Adam L MacLean Huda Nassar Christopher Rackauckas Timothy E Holy Michael P H Stumpf School of Mathematics and Statistics University of Melbourne Melbourne Victoria Australia. Melbourne Integrative Genomics University of Melbourne Melbourne Victoria Australia. Medical Research Council Laboratory of Molecular Biology Cambridge UK. Department of Quantitative and Computational Biology University of Southern California Los Angeles CA USA. RelationalAI Berkeley CA USA. Department of Mathematics Massachusetts Institute of Technology Cambridge MA USA. Julia Computing Somerville MA USA. Pumas-AI Centreville VA USA. Departments of Neuroscience and Biomedical Engineering Washington University in St. Louis St. Louis MO USA. School of Mathematics and Statistics University of Melbourne Melbourne Victoria Australia. mstumpf@unimelb.edu.au. Melbourne Integrative Genomics University of Melbourne Melbourne Victoria Australia. mstumpf@unimelb.edu.au. School of BioSciences The University of Melbourne Melbourne Victoria Australia. mstumpf@unimelb.edu.au. ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems Melbourne Victoria Australia. mstumpf@unimelb.edu.au.

Density matrix based perturbative corrections for improved quantum simulation accuracy

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arXiv 2019年

作者： Morris, T.D. Parks, Z.P. McCaskey, A.J. Jakowski, J. Pooser, R.C. Physics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Quantum Computing Institute Oak Ridge National Laboratory Oak RidgeTN37831 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

We present error mitigation (EM) techniques for noisy intermediate-scale quantum computers (QC) based on density matrix purification and perturbative corrections to the target energy. We incorporate this scheme into the variational quantum eigensolver (VQE) and demonstrate chemically-accurate ground state energy calculations of various alkali metal hydrides using IBM quantum computers. Both the density matrix purification improvements and the perturbative corrections require only meager classical computational resources, and are conducted exclusively as post-processing of the measured density matrix. The improved density matrix leads to better simulation accuracy at each step of the variational optimization, resulting in a better input into the next optimization step without additional measurements. Adding perturbative corrections to the resulting energies further increases the accuracy, and decreases variation between consecutive measurements. These EM schemes allow for previously unavailable levels of accuracy over remote QC resources. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

Influence of the vacuum polarization effect on the motion of charged particles in the magnetic field around a Schwarzschild Black Hole

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arXiv 2019年

作者： Pavlović, Petar Saveliev, Andrey Sossich, Marko Department of Physics Ramashna Mission Vivekananda Educational and Research Institute Belur MathWest Bengal711202 India Institute of Physics Mathematics and Information Technology Immanuel Kant Baltic Federal University Ul. Aleksandra Nevskogo 14 Kaliningrad236016 Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University GSP-1 Leninskiye Gory 1-52 Moscow119991 Russia Faculty of Electrical Engineering and Computing Department of Physics University of Zagreb Unska 3 Zagreb10 000 Croatia

The consequences of the vacuum polarization effect in magnetic fields around a Schwarzschild Black Hole (SBH) on the motion of charged particles are investigated in this work. Using the weak electromagnetic field approximation, we discuss the non-minimal coupling between magnetic fields and gravity caused by the vacuum polarization and study the equations of motion for the case of a magnetic field configuration which asymptotically approaches a dipole magnetic field. It is shown that the presence of non-minimal coupling can significantly influence the motion of charged particles around Black Holes. In particular, the vacuum polarization effect, leading to strong amplification or suppression of the magnetic field strength around the event horizon (depending on the sign of the coupling parameter), can affect the scattering angle and minimal distance for the electrons moving in the gravitational field of the Black Hole as well as the dependence of these parameters on the asymptotic magnetic field strength, initial distance and the Black Hole mass. It is further demonstrated that the non-minimal coupling between gravity and astrophysical magnetic fields, caused by the vacuum polarization, can cause significant changes of the parameter space corresponding to bound trajectories around the Black Hole. In certain cases, the bounded or unbounded character of a trajectory is determined solely by the presence of non-minimal coupling and its strength. These effects could in principle be used as observational signatures of the vacuum polarization effect and also to constrain the value of the coupling parameter. Copyright © 2019, The Authors. All rights reserved.

关键词： Gravitation

Peter H. Rose - Father of Ion Implantation: The Early Years

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Peter H. Rose - Father of Ion Implantation: The Early Years

International Conference on Ion Implantation Technology (IIT)

作者： Andrew Wittkower Geoffrey Ryding ESAT/STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In 1996, President Clinton presented Peter Rose with the National Medal of Technology and Innovation. This paper describes his innovations in ion implantation technology which led to the award presented twenty years l... 详细信息

ISBN: (纸本)9781538668306

关键词： Ion implantation Ion sources Business Technological innovation Acceleration Particle beams

Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor

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arXiv 2020年

作者： Hashim, Akel Naik, Ravi K. Morvan, Alexis Ville, Jean-Loup Mitchell, Bradley Kreikebaum, John Mark Davis, Marc Smith, Ethan Iancu, Costin O'Brien, Kevin P. Hincks, Ian Wallman, Joel J. Emerson, Joseph Siddiqi, Irfan Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Graduate Group in Applied Science and Technology University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Quantum Benchmark Inc. KitchenerONN2H 5G5 Canada Institute for Quantum Computing and Department of Applied Mathematics University of Waterloo WaterlooONN2L 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 which 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. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking