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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Terahertz Cavity Phonon Polaritons in Lead Telluride in the Deep-Strong Coupling Regime

arXiv

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arXiv 2025年

作者： Baydin, Andrey Manjappa, Manukumara Mishra, Sobhan Subhra Xu, Hongjing Doumani, Jacques Tay, Fuyang Kim, Dasom Rappl, Paulo H.O. Abramof, Eduardo Singh, Ranjan Hernandez, Felix G.G. Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley–Curl Institute Rice University HoustonTX77005 United States Rice Advanced Materials Institute Rice University HoustonTX77005 United States Instrumentation and Applied Physics Department Indian Institute of Science Bangalore560012 India Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University 639798 Singapore Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Group of Research and Development in Materials and Plasma National Institute for Space Research São José dos Campos São Paulo12201-970 Brazil Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Instituto de Física Universidade de São Paulo São Paulo05508-090 Brazil Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

Lead telluride is an important thermoelectric material due to its large Seebeck coefficient combined with its unusually low thermal conductivity that is related to the strong anharmonicity of phonons in this material. Here, we have studied the resonant and nonperturbative coupling of transverse optical phonons in lead telluride with cavity photons inside small-mode-volume metallic metasurface cavities that have photonic modes with terahertz frequencies. We observed a giant vacuum Rabi splitting on the order of the bare phonon and cavity frequencies. Through terahertz time-domain spectroscopy experiments, we systematically studied the vacuum Rabi splitting as a function of sample thickness, temperature, and cavity length. Under the strongest light–matter coupling conditions, the strength of coupling exceeded the bare phonon and cavity frequencies, putting the system into the deep-strong coupling regime. These results demonstrate that this uniquely tunable platform is promising for realizing and understanding predicted cavity-vacuum-induced ferroelectric instabilities and exploring applications of light–matter coupling in the ultrastrong and deep-strong coupling regimes in quantum technology. Copyright © 2025, The Authors. All rights reserved.

关键词： Phonons

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On Estimation and Cross–validation of Dynamic Treatment Regimes with Competing Risks

arXiv

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arXiv 2021年

作者： Morzywolek, Pawel Steen, Johan Van Biesen, Wim Decruyenaere, Johan Vansteelandt, Stijn Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Department of Internal Medicine and Pediatrics Ghent University Belgium Renal Division Ghent University Hospital Belgium Department of Intensive Care Medicine Ghent University Hospital Belgium Department of Medical Statistics London School of Hygiene and Tropical Medicine United Kingdom

The optimal moment to start renal replacement therapy in a patient with acute kidney injury (AKI) remains a challenging problem in intensive care nephrology. Multiple randomised controlled trials have tried to answer this question, but these contrast only a limited number of treatment initiation strategies. In view of this, we use routinely collected observational data from the Ghent University Hospital intensive care units (ICUs) to investigate different prespecified timing strategies for renal replacement therapy initiation based on time–updated levels of serum potassium, pH and fluid balance in critically ill patients with AKI with the aim to minimize 30–day ICU mortality. For this purpose, we apply statistical techniques for evaluating the impact of specific dynamic treatment regimes in the presence of ICU discharge as a competing event. We discuss two approaches, a nonparametric one – using an inverse probability weighted Aalen–Johansen estimator – and a semiparametric one – using dynamic–regime marginal structural models. Furthermore, we suggest an easy to implement cross–validation technique to assess the out–of–sample performance of the optimal dynamic treatment regime. Our work illustrates the potential of data–driven medical decision support based on routinely collected observational data. Copyright © 2021, The Authors. All rights reserved.

关键词： Intensive care units

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Iterative quantum optimization of spin glass problems with rapidly oscillating transverse fields

arXiv

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arXiv 2024年

作者： Barton, Brandon Sagal, Jacob Feeney, Sean Grattan, George Patnaik, Pratik Oganesyan, Vadim Carr, Lincoln D. Kapit, Eliot Quantum Engineering Program Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Computer Science Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Physics and Astronomy College of Staten Island City University of New York Staten IslandNY10314 United States Physics program and Initiative for the Theoretical Sciences The Graduate Center City University of New York New YorkNY10016 United States

In this work, we introduce a new iterative quantum algorithm, called Iterative Symphonic Tunneling for Satisfiability problems (IST-SAT), which solves quantum spin glass optimization problems using high-frequency oscillating transverse fields. IST-SAT operates as a sequence of iterations, in which bitstrings returned from one iteration are used to set spin-dependent phases in oscillating transverse fields in the next iteration. Over several iterations, the novel mechanism of the algorithm steers the system toward the problem ground state. We benchmark IST-SAT on sets of hard MAX-3-XORSAT problem instances with exact state vector simulation, and report polynomial speedups over trotterized adiabatic quantum computation (TAQC) and the best known semi-greedy classical algorithm. When IST-SAT is seeded with a sufficiently good initial approximation, the algorithm converges to exact solution(s) in a polynomial number of iterations. Our numerical results identify a critial Hamming radius(CHR), or quality of initial approximation, where the time-to-solution crosses from exponential to polynomial scaling in problem size. By combining IST-SAT with future classical or quantum approximation algorithms, larger gains may be achieved. The mechanism we present in this work thus presents a new path toward achieving quantum advantage in optimization. © 2024, CC BY.

关键词： Combinatorial optimization

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The spatial zonation of the murine placental vasculature is specified by epigenetic mechanisms

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Developmental Cell 2025年第10期60卷 1467-1482.e8页

作者： Gehrs, Stephanie Jakab, Moritz Gutjahr, Ewgenija Gu, Zuguang Weichenhan, Dieter Mallm, Jan-Philipp Mogler, Carolin Schlesner, Matthias Plass, Christoph Schlereth, Katharina Augustin, Hellmut G. Division of Vascular Oncology and Metastasis German Cancer Research Center (DKFZ) Heidelberg 69120 Germany European Center for Angioscience (ECAS) Medical Faculty Mannheim Heidelberg University Mannheim 68167 Germany Faculty of Biosciences Heidelberg University Heidelberg 69120 Germany Institute of Pathology University Clinic Heidelberg Heidelberg 69120 Germany Computational Oncology Group Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Cancer Epigenomics German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and Bioquant Heidelberg 69120 Germany Institute of Pathology TUM School of Medicine Technical University of Munich Munich 80333 Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg 86159 Germany

The labyrinthian fetoplacental capillary network is vital for proper nourishment of the developing embryo. Dysfunction of the maternal-fetal circulation is a primary cause of placental insufficiency. Here, we show that the spatial zonation of the murine placental labyrinth vasculature is controlled by flow-regulated epigenetic mechanisms. Spatiotemporal transcriptomic profiling identified a gradual change in the expression of epigenetic enzymes, including the de novo DNA methyltransferase 3a (DNMT3A). Loss of Dnmt3a resulted in DNA hypomethylation and perturbation of zonated placental gene expression. The resulting global DNA hypomethylation impaired the angiogenic capacity of endothelial cells. Global or endothelium-predominant deletion of Dnmt3a resulted in impaired placental vascularization and fetal growth retardation (FGR). Human placental endothelial gene expression profiling associated preeclampsia with reduced DNMT3A expression. Collectively, our study identified DMNT3A as critical methylome-regulator of placental endothelial gene expression and function with clinical implications for placental dysfunction, as it occurs during preeclampsia or FGR. © 2024 The Authors

关键词： angiogenesis DNA methylation DNMT3A epigenetic regulation growth retardation placental endothelium spatial zonation

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Feynman checkers: towards algorithmic quantum theory

arXiv

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arXiv 2020年

作者： Skopenkov, Mikhail Ustinov, A. HSE University Faculty of Mathematics Institute for Information Transmission Problems Russian Academy of Sciences Russia HSE University Faculty of Computer Science Khabarovsk Division of the Institute for Applied Mathematics Far-Eastern Branch Russian Academy of Sciences Russia

We survey and develop the most elementary model of electron motion introduced by ***. In this game, a checker moves on a checkerboard by simple rules, and we count the turns. Feynman checkers are also known as a one-dimensional quantum walk or an Ising model at imaginary temperature. We solve mathematically a problem by *** from 1965, which was to prove that the discrete model (for large time, small average velocity, and small lattice step) is consistent with the continuum one. We study asymptotic properties of the model (for small lattice step and large time) improving the results by *** from 1972 and by *** from 2012. For the first time we observe and prove concentration of measure in the small-lattice-step limit. We perform the second quantization of the *** Codes 82B20, 11L03, 68Q12, 81P68, 81T25, 81T40, 05A17, 11P82, 33C45 Copyright © 2020, The Authors. All rights reserved.

关键词： Ising model

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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TAROGE-M: Antarctic High-Mountain Radio Antenna Array for Detecting Ultra-High Energy ANITA Anomalous Events 38

TAROGE-M: Antarctic High-Mountain Radio Antenna Array for De...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Wang, Shih-Hao Chen, Pisin Chen, Yaocheng Chen, Ying-Chih Choi, Taejin Ham, Young-Bae Hsu, Shih-Ying Huang, Jian-Jung Huang, MingHuey A. Jee, Geonhwa Jung, Jongil Kim, Jieun Kuo, Chung-Yun Kwon, Hyuck-Jin Lee, Changsup Leung, Chung-Hei Liu, Tsung-Che Nam, Jiwoo Shiao, Yu-Shao J. Shin, Bok-Kyun Su, Shih-Chieh Wang, Min-Zu Wang, Shih-Hao Wang, Yu-Hsin Anker, Astrid Barwick, Steven W. Besson, Dave Z. Bouma, Sjoerd Cataldo, Maddalena Gaswint, Geoffrey Glaser, Christian Hallmann, Steffen Hanson, Jordan C. Henrichs, Jakob Kleinfelder, Stuart A. Lahmann, Robert Meyers, Zachary S. Nelles, Anna Novikov, Alexander Paul, Manuel P. Pyras, Lilly Persichilli, Christopher Plaisier, Ilse Rice-Smith, Ryan Seikh, Mohammad F. H. Tatar, Joulien Welling, Christoph Zhao, Leshan Department of Physics National Taiwan University Taipei10617 Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei10617 Taiwan Kavli Institute for Particle Astrophysics and Cosmology SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Division of Atmospheric Sciences Korea Polar Research Institute Incheon Korea Republic of Department of Polar Science Korea University of Science and Technology Daejeon Korea Republic of Department of Energy Engineering National United University Miaoli Taiwan Department of Astronomy Space Science and Geology Chungnam National University Daejeon34134 Korea Republic of Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National Pingtung university Pingtung900 Taiwan Chung Cheng Institute of Technology National Defense University Taoyuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Nano Device Laboratories Hsinchu300 Taiwan Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Department of Physics and Astronomy University of California IrvineCA92697 United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States DESY Zeuthen15738 Germany ECAP Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden Whittier College Department of Physics WhittierCA90602 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Resea

TAROGE-M is a radio antenna array atop ∼ 2.7 km-high Mt. Melbourne in Antarctica for detecting ultra-high energy (UHE, E > 1017 eV) air showers in near-horizontal directions. Besides the detection of cosmic rays and Earth-skimming tau neutrinos, its primary goal is to reproduce the discovery and verify the origin of so-called ANITA anomalous events, having feature of upward-going UHE air showers but cannot be explained by tau neutrinos. The detection concept takes advantages of a high altitude for a broad view toward the horizon;strong and near-vertical geomagnetic field for enhancing the radio signal;and quiet radio environment in Antarctica. Its relatively simple design and high duty cycle make it easily to be extended and thus to achieve an exposure competitive with ANITA experiments within a few years. The first TAROGE-M station operating at 180-450 MHz frequencies deployed in 2020 detected seven UHE cosmic ray events within 25.3-day livetime. The events have a mean energy of ∼ 1 EeV and an estimated flux consistent with other experiments, and validate the station as an UHE particle detector. In 2022-2023 season, the system was upgraded for a robust long-term operation, with a more detailed calibration with a drone-borne pulser. We also discuss the planned upgrade, including the implementation of interferometric trigger, which is expected to increase the cosmic ray and tau neutrino acceptances by a factor of 10 at 0.1 EeV, corresponding to a lower energy threshold by a factor of 3. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Layer-dependent optically-induced spin polarization in InSe

arXiv

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arXiv 2022年

作者： Nelson, Jovan Stanev, Teodor K. Lebedev, Dmitry LaMountain, Trevor Tyler Gish, J. Zeng, Hongfei Shin, Hyeondeok Heinonen, Olle Watanabe, Kenji Taniguchi, Takashi Hersam, Mark C. Stern, Nathaniel P. Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Material Science & Engineering Northwestern University EvanstonIL60208 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States Material Science Division Argonne National Laboratory LemontIL60439 United States Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL60208 United States

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for opto-electronics due to its high electron mobility, tunable direct bandgap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules suggesting potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photo-luminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g-factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab-initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for opto-spintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material. Copyright © 2022, The Authors. All rights reserved.

关键词： Spin dynamics

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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

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