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arXiv 2022年

作者： Berlin, Asher Belomestnykh, Sergey Blas, Diego Frolov, Daniil Brady, Anthony J. Braggio, Caterina Carena, Marcela Cervantes, Raphael Checchin, Mattia Contreras-Martinez, Crispin D’Agnolo, Raffaele Tito Ellis, Sebastian A.R. Eremeev, Grigory Gao, Christina Giaccone, Bianca Grassellino, Anna Harnik, Roni Hollister, Matthew Janish, Ryan Kahn, Yonatan Kazakov, Sergey Kurkcuoglu, Doga Murat Liu, Zhen Lunin, Andrei Netepenko, Alexander Melnychuk, Oleksandr Pilipenko, Roman Pischalnikov, Yuriy Posen, Sam Romanenko, Alex Schütte-Engel, Jan Wang, Changqing Yakovlev, Vyacheslav Zhou, Kevin Zorzetti, Silvia Zhuang, Quntao Fermi National Accelerator Laboratory BataviaIL60510 United States Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Applied Physics and Superconducting Technology Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Stony Brook University Stony BrookNY11794 United States Grup de Física Teòrica Departament de Física Universitat Autònoma de Barcelona Bellaterra Barcelona08193 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States Dipartimento di Fisica e Astronomia Padova Italy INFN Sezione di Padova Padova Italy Department of Physics University of Chicago ChicagoIL60637 United States Université Paris Saclay CEA CNRS Institut de Physique Théorique Gif-sur-Yvette 91191 France Département de Physique Théorique Université de Genève 24 quai Ernest Ansermet Genève 41211 Switzerland Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Fermilab Quantum Institute Fermi National Accelerator Laboratory BataviaIL60510 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States J. C. Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States

This is a Snowmass white paper on the utility of existing and future superconducting cavities to probe fundamental physics. Superconducting radio frequency (SRF) cavity technology has seen tremendous progress in the past decades, as a tool for accelerator science. With advances spearheaded by the SQMS center at Fermilab, they are now being brought to the quantum regime becoming a tool in quantum science thanks to the high degree of coherence. The same high quality factor can be leveraged in the search for new physics, including searches for new particles, dark matter, including the QCD axion, and gravitational waves. We survey some of the physics opportunities and the required directions of R&D. Given the already demonstrated integration of SRF cavities in large accelerator systems, this R&D may enable larger scale searches by dedicated experiments. © 2022, CC0.

关键词： Galaxies

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Toward a resolution of the NN controversy

arXiv

引用

arXiv 2021年

作者： Nicholson, Amy Berkowitz, Evan Bulava, John Chang, Chia Cheng Clark, M.A. Hanlon, Andrew D. Hörz, Ben Howarth, Dean Körber, Christopher Lee, Wayne Tai Meyer, Aaron S. Monge-Camacho, Henry Morningstar, Colin Rinaldi, Enrico Vranas, Pavlos Walker-Loud, André Department of Physics and Astronomy University of North Carolina Chapel HillNC27516-3255 United States Intel Corporation Santa ClaraCA95054 United States Physics Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Physics Department University of Michigan Ann ArborMI48109 United States Theoretical Quantum Physics Laboratory RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan Physics Department Brookhaven National Laboratory UptonNY11973 United States Institut für Theoretische Physik II Ruhr-Universität Bochum BochumD-44780 Germany Department of Physics University of California BerkeleyCA94720 United States Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States CP3 -Origins Dept. of Mathematics and Computer Science University of Southern Denmark Campusvej 55 Odense M5230 Denmark NVIDIA Corporation Santa ClaraCA95050 United States Department of Statistics Columbia University New YorkNY10027 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States Institut für Kernphysik Institute for Advanced Simulation Forschungszentrum Jülich Jülich54245 Germany Escuela de Física Universidad de Costa Rica San Pedro San José11501 Costa Rica iTHEMS RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan Center for Quantum Computing RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

Lattice QCD calculations of two-nucleon interactions have been underway for about a decade, but still haven’t reached the pion mass regime necessary for matching onto effective field theories and extrapolating to the physical point. Furthermore, results from different methods, including the use of the Lüscher formalism with different types of operators, as well as the HALQCD potential method, do not agree even qualitatively at very heavy pion mass. We investigate the role that different operators employed in the literature may play on the extraction of spectra for use within the Lüscher method. We first explore expectations from Effective Field theory solved within a finite volume, for which the exact spectrum may be computed given different physical scenarios. We then present preliminary lattice QCD results for two-nucleon spectra calculated using different operators on a common lattice ensemble. Copyright © 2021, The Authors. All rights reserved.

关键词： Hadrons

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quantum chemistry as a benchmark for near-term quantum computers

arXiv

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arXiv 2019年

作者： McCaskey, Alexander J. Parks, Zachary P. Jakowski, Jacek Moore, Shirley V. Morris, T. Humble, Travis S. Pooser, Raphael C. 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 Physics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

We present a quantum chemistry benchmark for noisy intermediate-scale quantum computers that leverages the variational quantum eigensolver, active space reduction, a reduced unitary coupled cluster ansatz, and reduced density purification as error mitigation. We demonstrate this benchmark on the 20 qubit IBM Tokyo and 16 qubit Rigetti Aspen processors via the simulation of alkali metal hydrides (NaH, KH, RbH),with accuracy of the computed ground state energy serving as the primary benchmark metric. We further parameterize this benchmark suite on the trial circuit type, the level of symmetry reduction, and error mitigation strategies. Our results demonstrate the characteristically high noise level present in near-term superconducting hardware, but provide a relevant baseline for future improvement of the underlying hardware, and a means for comparison across near-term hardware types. We also demonstrate how to reduce the noise in post processing with specific error mitigation techniques. Particularly, the adaptation of McWeeny purification of noisy density matrices dramatically improves accuracy of quantum computations, which, along with adjustable active space, significantly extends the range of accessible molecular systems. We demonstrate that for specific benchmark settings, the accuracy metric can reach chemical accuracy when computing over the cloud on certain quantum computers. Copyright © 2019, The Authors. All rights reserved.

关键词： quantum chemistry

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Efficient quantum algorithms for simulating lindblad evolution 44

Efficient quantum algorithms for simulating lindblad evoluti...

引用

44th International Colloquium on Automata, Languages, and Programming, ICALP 2017

作者： Cleve, Richard Wang, Chunhao Institute for Quantum Computing University of Waterloo Waterloo Canada Cheriton School of Computer Science University of Waterloo Waterloo Canada Canadian Institute for Advanced Research Toronto Canada

ISBN: (纸本)9783959770415

We consider the natural generalization of the Schrödinger equation to Markovian open system dynamics: the so-called the Lindblad equation. We give a quantum algorithm for simulating the evolution of an n-qubit system for time t within precision ϵ. If the Lindbladian consists of poly(n) operators that can each be expressed as a linear combination of poly(n) tensor products of Pauli operators then the gate cost of our algorithm is O(t polylog(t/ϵ)poly(n)). We also obtain similar bounds for the cases where the Lindbladian consists of local operators, and where the Lindbladian consists of sparse operators. This is remarkable in light of evidence that we provide indicating that the above efficiency is impossible to attain by first expressing Lindblad evolution as Schrödinger evolution on a larger system and tracing out the ancillary system: the cost of such a reduction incurs an efficiency overhead of O(t2/ϵ) even before the Hamiltonian evolution simulation begins. Instead, the approach of our algorithm is to use a novel variation of the "linear combinations of unitaries" construction that pertains to channels. © Richard Cleve and Chunhao Wang;.

关键词： Hamiltonians

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Structure optimization for parameterized quantum circuits

arXiv

引用

arXiv 2019年

作者： Ostaszewski, Mateusz Grant, Edward Benedetti, Marcello Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Department of Computer Science University College London LondonWC1E 6BT United Kingdom Rahko Limited LondonN4 3JP United Kingdom Cambridge Quantum Computing Limited CambridgeCB2 1UB United Kingdom

We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits that use structure optimization perform significantly better than circuits that use parameter updates alone, making this method particularly suitable for noisy intermediate-scale quantum computers. We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer. © 2019, CC BY.

关键词： Structural optimization

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Dimension crossing turbulent cascade in an excited lattice bose gas

arXiv

引用

arXiv 2019年

作者： Zhou, Tianwei Yao, Ruixiao Yang, Kaixiang Jin, Shengjie Zhai, Yueyang Yue, Xuguang Yang, Shifeng Zhou, Xiaoji Chen, Xuzong Li, Xiaopeng School of Electronics Engineering and Computer Science Peking University Beijing100871 China Innovative Research Institute of Frontier Science and Technology Beihang University Beijing100191 China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Chinese Academy of Sciences Wuhan430071 China State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computing Department of Physics Fudan University Shanghai200433 China INO-CNR Istituto Nazionale di Ottica del CNR Sezione di Sesto Fiorentino Sesto FiorentinoI-50019 Italy

Turbulence is an intriguing non-equilibrium state, which originates from fluid mechanics and has far-reaching consequences in the description of climate physics, the characterization of quantum hydrodynamics, and the understanding of cosmic evolution. The concept of turbulent cascade describing the energy redistribution across different length scales offers one profound route to reconcile fundamental conservative forces with observational energy non-conservation of accelerating expansion of the universe bypassing the cosmological constant. Here, we observe a dimension crossing turbulent energy cascade in an atomic Bose-Einstein condensate confined in a two-dimensional (2d) optical lattice forming a 2d array of tubes, which exhibits universal behaviors in the dynamical energy-redistribution across different dimensions. By exciting atoms into the optical-lattice high bands, the excessive energy of this quantum many-body system is found to cascade from the transverse two-dimensional lattice directions to the continuous dimension, giving rise to a one-dimensional turbulent energy cascade, which is in general challenging to reach due to integrability. We expect this observed novel phenomenon of dimension-crossing energy cascade may inspire microscopic theories for modeling positive cosmological constant of our inflationary universe. Copyright © 2019, The Authors. All rights reserved.

关键词： Bose-Einstein condensation

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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quantum computer systems for scientific discovery

arXiv

引用

arXiv 2019年

作者： Alexeev, Yuri Bacon, Dave Brown, Kenneth R. Calderbank, Robert Carr, Lincoln D. Chong, Frederic T. DeMarco, Brian Englund, Dirk Farhi, Edward Fefferman, Bill Gorshkov, Alexey V. Houck, Andrew Kim, Jungsang Kimmel, Shelby Lange, Michael Lloyd, Seth Lukin, Mikhail D. Maslov, Dmitri Maunz, Peter Monroe, Christopher Preskill, John Roetteler, Martin Savage, Martin J. Thompson, Jeff Argonne National Laboratory LemontIL United States Google Inc. SeattleWA United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Chemistry Duke University DurhamNC United States Department of Physics Duke University DurhamNC United States Department of Computer Science Department of Mathematics Duke University DurhamNC United States Department of Physics Colorado School of Mines GoldenCO United States Department of Computer Science University of Chicago ChicagoIL United States Department of Physics and IQUIST University of Illinois Urbana-ChampaignIL United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Google Inc. VeniceCA United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States Joint Quantum Institute Joint Center for Quantum Information and Computer Science Department of Physics University of Maryland College ParkMD United States National Institute of Standards and Technology GaithersburgMD United States Department of Electrical Engineering Princeton University PrincetonNJ United States IonQ Inc. College ParkMD United States Department of Computer Science Middlebury College MiddleburyVT United States L3Harris Technologies MelboruneFL Australia Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Physics Harvard University CambridgeMA United States IBM T.J. Watson Research Center Yorktown HeightsNY United States Sandia National Laboratories AlbuquerqueNM United States Institute for Quantum Information and Matter Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA United States Microsoft Quantum RedmondWA United States Institute for Nuclear Theory and Department of Physics University of Washington SeattleWA United States

The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by co-designing full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2–10 years. Copyright © 2019, The Authors. All rights reserved.

关键词： Qubits

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quantum-centric Supercomputing for Materials science: A Perspective on Challenges and Future Directions

arXiv

引用

arXiv 2023年

作者： Alexeev, Yuri Amsler, Maximilian Barroca, Marco Antonio Bassini, Sanzio Battelle, Torey Camps, Daan Casanova, David Choi, Young Jai Chong, Frederic T. Chung, Charles Codella, Christopher Córcoles, Antonio D. Cruise, James Di Meglio, Alberto Duran, Ivan Eckl, Thomas Economou, Sophia Eidenbenz, Stephan Elmegreen, Bruce Fare, Clyde Faro, Ismael Fernández, Cristina Sanz Ferreira, Rodrigo Neumann Barros Fuji, Keisuke Fuller, Bryce Gagliardi, Laura Galli, Giulia Glick, Jennifer R. Gobbi, Isacco Gokhale, Pranav de la Puente Gonzalez, Salvador Greiner, Johannes Gropp, Bill Grossi, Michele Gull, Emanuel Healy, Burns Hermes, Matthew R. Huang, Benchen Humble, Travis S. Ito, Nobuyasu Izmaylov, Artur F. Javadi-Abhari, Ali Jennewein, Douglas Jha, Shantenu Jiang, Liang Jones, Barbara de Jong, Wibe Albert Jurcevic, Petar Kirby, William Kister, Stefan Kitagawa, Masahiro Klassen, Joel Klymko, Katherine Koh, Kwangwon Kondo, Masaaki Kürkçüoglu, Doga Murat Kurowski, Krzysztof Laino, Teodoro Landfield, Ryan Leininger, Matt Leyton-Ortega, Vicente Li, Ang Lin, Meifeng Liu, Junyu Lorente, Nicolas Luckow, Andre Martiel, Simon Martin-Fernandez, Francisco Martonosi, Margaret Marvinney, Claire Medina, Arcesio Castaneda Merten, Dirk Mezzacapo, Antonio Michielsen, Kristel Mitra, Abhishek Mittal, Tushar Moon, Kyungsun Moore, Joel Mostame, Sarah Motta, Mario Na, Young-Hye Nam, Yunseong Narang, Prineha Ohnishi, Yu-Ya Ottaviani, Daniele Otten, Matthew Pakin, Scott Pascuzzi, Vincent R. Pednault, Edwin Piontek, Tomasz Pitera, Jed Rall, Patrick Ravi, Gokul Subramanian Robertson, Niall Rossi, Matteo A.C. Rydlichowski, Piotr Ryu, Hoon Samsonidze, Georgy Sato, Mitsuhisa Saurabh, Nishant Sharma, Vidushi Sharma, Kunal Shin, Soyoung Slessman, George Steiner, Mathias Sitdikov, Iskandar Suh, In-Saeng Switzer, Eric D. Tang, Wei Thompson, Joel Todo, Synge Tran, Minh C. Trenev, Dimitar Trott, Christian Tseng, Huan-Hsin Tubman, Norm M. Tureci, Esin Valiñas, David García Vallecorsa, Sofia Wever, Christopher Wojciechowski, Konrad Wu, Xiaodi Yoo, Shinjae Yoshioka, Argonne National Laboratory LemontIL60439 United States Corporate Sector Research and Advance Engineering Robert Bosch GmbH Robert-Bosch-Campus 1 RenningenD-71272 Germany IBM Research RJ Rio de Janeiro20031-170 Brazil Centro Brasileiro de Pesquisas Físicas RJ Rio de Janeiro22290-180 Brazil CINECA via Magnanelli 6/3 BO Casalecchio di Reno40033 Italy Arizona State University TempeAZ United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Euskadi Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Physics Yonsei University Seoul03722 Korea Republic of University of Chicago ChicagoIL United States IBM Quantum IBM T.J. Watson Research Center Yorktown Heights NY10598 United States Cambridge Consultants part of Capgemini Invent Cambridge United Kingdom Geneva1211 Switzerland Virginia Tech BlacksburgVA24061 United States Los Alamos National Laboratory Los AlamosNM87545 United States Osaka University Osaka560-8531 Japan Department of Chemistry Chicago Center for Theoretical Chemistry University of Chicago ChicagoIL United States Fraunhofer ITWM Rheinland-Pfalz Kaiserslautern67663 Germany Infleqtion ChicagoIL60622 United States University of Illinois Urbana-Champaign United States University of Michigan Ann ArborMI48109 United States Research Office Oak Ridge National Laboratory One Bethel Valley Road Oak RidgeTN37831 United States Hyogo Kobe650-0047 Japan Chemical Physics Theory Group Department of Chemistry University of Toronto TorontoONM5S 3H6 Canada Department of Physical and Environmental Sciences University of Toronto Scarborough TorontoONM1C 1A4 Canada Brookhaven National Laboratory UptonNY United States Rutgers University New BrunswickNJ United States IBM Quantum Almaden Research Center San JoseCA95120 United States Applied Mathematics and Computational Research Division

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Characterization (materials science)

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Lithium-Ion Batteries: Operando Magnetometry Probing the Charge Storage Mechanism of CoO Lithium-Ion Batteries (Adv. Mater. 12/2021)

引用

Advanced Materials 2021年第12期33卷

作者： Hongsen Li Zhengqiang Hu Qingtao Xia Hao Zhang Zhaohui Li Huaizhi Wang Xiangkun Li Fengkai Zuo Fengling Zhang Xiaoxiong Wang Wanneng Ye Qinghao Li Yunze Long Qiang Li Shishen Yan Xiaosong Liu Xiaogang Zhang Guihua Yu Guo-Xing Miao College of Physics Center for Marine Observation and Communications Qingdao University Qingdao 266071 China School of Physics State Key Laboratory of Crystal Materials Shandong University Jinan 250100 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences Shanghai 200050 China College of Material Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Materials Science and Engineering Program and Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA Department of Electrical and Computer Engineering & Institute for Quantum Computing University of Waterloo Waterloo ON N2L 3G1 Canada

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