检索结果-内蒙古大学图书馆

Direct measurement of Bacon-Shor code stabilizers

学校读者我要写书评

暂无评论

Physical Review A 2018年第5期98卷 050301(R)-050301(R)页

作者： Muyuan Li Daniel Miller Kenneth R. Brown School of Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA Institut für Theoretische Physik III Heinrich-Heine-Universität Düsseldorf D-40225 Düsseldorf Germany Departments of Electrical and Computer Engineering Chemistry and Physics Duke University Durham North Carolina 27708 USA

A Bacon-Shor code is a subsystem quantum error-correcting code on an L×L lattice where the 2(L−1) weight-2L stabilizers are usually inferred from the measurements of 2L(L−1) weight-2 gauge operators. Here, we show that the stabilizers can be measured directly and fault tolerantly with bare ancillary qubits by constructing circuits that follow the pattern of gauge operators. We then examine the implications of this method for small quantum error-correcting codes by comparing distance-3 versions of the rotated surface code and the Bacon-Shor code with the standard depolarizing model and in the context of a trapped-ion quantum computer. We find that for a simple circuit of prepare, error correct, and measure, the Bacon-Shor code outperforms the surface code by requiring fewer qubits, taking less time, and having a lower error rate.

关键词： Quantum computation Quantum error correction Surface code quantum computing Topological quantum computing

Quantum adiabatic machine learning with zooming

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Zlokapa, Alexander Mott, Alex Job, Joshua Vlimant, Jean-Roch Lidar, Daniel Spiropulu, Maria Division of Physics Mathematics & Astronomy Alliance for Quantum Technologies California Institute of Technology PasadenaCA91125 United States DeepMind Technologies London United Kingdom Lockheed Martin Corporation El SegundoCA90245 United States Departments of Electrical and Computer Engineering Chemistry and Physics & Astronomy Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States

Recent work has shown that quantum annealing for machine learning (QAML) can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose a variant algorithm (QAML-Z) that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z increases the performance difference between QAML and classical deep neural networks by over 40% as measured by area under the ROC curve for small training set sizes. Furthermore, QAML-Z reduces the advantage of deep neural networks over QAML for large training sets by around 50%, indicating that QAML-Z produces stronger classifiers that retain the robustness of the original QAML algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

3D Microstructures: Transparent, Compliant 3D Mesostructures for Precise Evaluation of Mechanical Characteristics of Organoids (Adv. Mater. 25/2021)

学校读者我要写书评

暂无评论

Advanced Materials 2021年第25期33卷

作者： Hanjun Ryu Yoonseok Park Haiwen Luan Gokhan Dalgin Kira Jeffris Hong-Joon Yoon Ted S. Chung Jong Uk Kim Sung Soo Kwak Geumbee Lee Hyoyoung Jeong Jihye Kim Wubin Bai Joohee Kim Yei Hwan Jung Andrew K. Tryba Joseph W. Song Yonggang Huang Louis H. Philipson John D. Finan John A. Rogers Querrey Simpson Institute for Bioelectronics Northwestern University Evanston IL 60208 USA School of Advanced Materials Science and Engineering Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea Section of Adult and Pediatric Endocrinology Diabetes and Metabolism Kovler Diabetes Center The University of Chicago Chicago IL 60637 USA Department of Mechanical and Industrial Engineering University of Illinois at Chicago Chicago IL 60607 USA Department of Biomedical Engineering Northwestern University Evanston IL 60208 USA School of Chemical Engineering Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel Hill NC 27599 USA Department of Electronic Engineering Hanyang University Seoul 04763 Republic of Korea Section of Pediatric Neurology Department of Pediatrics The University of Chicago Chicago IL 60637 USA Center for Bio-Integrated Electronics Northwestern University Evanston IL 60208 USA Department of Mechanical Engineering Northwestern University Evanston IL 60208 USA Department of Civil and Environmental Engineering Northwestern University Evanston IL 60208 USA Department of Materials Science and Engineering Northwestern University Evanston IL 60208 USA Department of Medicine and Kovler Diabetes Center The University of Chicago Chicago IL 60637 USA Department of Chemistry Northwestern University Evanston IL 60208 USA Department of Electrical and Computer Engineering Northwestern University Evanston IL 60208 USA Departments of Neurological Surgery Feinberg School of Medicine Northwestern University Chicago IL 60611 USA

On the optimal achievable rates for linear computation with random homologous codes

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Sen, Pinar Lim, Sung Hoon Kim, Young-Han Department of Electrical and Computer Engineering University of California San Diego La JollaCA92093 United States Korea Institute of Ocean Science and Technology Busan49111 Korea Republic of

The problem of computing a linear combination of sources over a multiple access channel is studied. Inner and outer bounds on the optimal tradeoff between the communication rates are established when encoding is restricted to random ensembles of homologous codes, namely, structured nested coset codes from the same generator matrix and individual shaping functions, but when decoding is optimized with respect to the realization of the encoders. For the special case in which the desired linear combination is "matched" to the structure of the multiple access channel in a natural sense, these inner and outer bounds coincide. This result indicates that most, if not all, coding schemes for computation in the literature that rely on random construction of nested coset codes cannot be improved by using more powerful decoders, such as the maximum likelihood decoder. The proof techniques are adapted to characterize the rate region for broadcast channels achieved by Marton’s (random) coding scheme under maximum likelihood decoding. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

Accelerating MR imaging via Deep Chambolle-Pock network

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Wang, Haifeng Cheng, Jing Jia, Sen Qiu, Zhilang Shi, Caiyun Zou, Lixian Su, Shi Chang, Yuchou Zhu, Yanjie Ying, Leslie Liang, Dong Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China Computer Science Department University of Houston-Downtown HoustonTX77002 United States Biomedical Engineering and Electrical Engineering Departments State University of New York BuffaloNY14260 United States Research Center and Medical AI Paul C. LauterburResearch Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China

Compressed sensing (CS) has been introduced to accelerate data acquisition in MR Imaging. However, CS-MRI methods suffer from detail loss with large acceleration and complicated parameter selection. To address the limitations of existing CS-MRI methods, a model-driven MR reconstruction is proposed that trains a deep network, named CP-net, which is derived from the Chambolle-Pock algorithm to reconstruct the in vivo MR images of human brains from highly undersampled complex k-space data acquired on different types of MR scanners. The proposed deep network can learn the proximal operator and parameters among the Chambolle-Pock algorithm. All of the experiments show that the proposed CP-net achieves more accurate MR reconstruction results, outperforming state-of-the-art methods across various quantitative metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Information geometry of orthogonal initializations and training

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Sokól, Piotr Aleksander Park, Il Memming Department of Neurobiology and Behavior Departments of Applied Mathematics and Statistics Electrical and Computer Engineering Institutes for Advanced Computing Science and AI-driven Discovery and Innovation Stony Brook University Stony BrookNY11733

Recently mean field theory has been successfully used to analyze properties of wide, random neural networks. It gave rise to a prescriptive theory for initializing feed-forward neural networks with orthogonal weights, which ensures that both the forward propagated activations and the backpropagated gradients are near `2 isometries and as a consequence training is orders of magnitude faster. Despite strong empirical performance, the mechanisms by which critical initializations confer an advantage in the optimization of deep neural networks are poorly understood. Here we show a novel connection between the maximum curvature of the optimization landscape (gradient smoothness) as measured by the Fisher information matrix (FIM) and the spectral radius of the input-output Jacobian, which partially explains why more isometric networks can train much faster. Furthermore, given that orthogonal weights are necessary to ensure that gradient norms are approximately preserved at initialization, we experimentally investigate the benefits of maintaining orthogonality throughout training, from which we conclude that manifold optimization of weights performs well regardless of the smoothness of the gradients. Moreover, motivated by experimental results we show that a low condition number of the FIM is not predictive of faster learning. Copyright © 2018, The Authors. All rights reserved.

关键词： Fisher information matrix

Pet/ct radiomic sequencer for prediction of egfr and kras mutation status in nsclc patients

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Shiri, Isaac Maleki, Hassan Hajianfar, Ghasem Abdollahi, Hamid Ashrafinia, Saeed Oghli, Mostafa Ghelich Hatt, Mathieu Oveisi, Mehrdad Rahmim, Arman Department of Biomedical and Health Informatics Rajaie Cardiovascular Medical and Research Center Iran University of Medical Science Tehran Iran Research Center for Molecular and Cellular Imaging Tehran University of Medical Sciences Tehran Iran Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Radiologic Sciences and Medical Physics Faculty of Allied Medicine Kerman University Department of Radiology and Radiological Science Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States INSERM UMR 1101 LaTIM Univ Brest BrestF-29238 France Department of Computer Science University of British Columbia VancouverBC Canada Departments of Radiology and Physics & Astronomy University of British Columbia VancouverBC Canada Department of Integrative Oncology BC Cancer Research Centre VancouverBC Canada

The aim of this study was to develop radiomic models using PET/CT radiomic features with different machine learning approaches for finding best predictive epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS) mutation status. Patient’s images including PET and CT [diagnostic (CTD) and low dose CT (CTA)] were pre-processed using wavelet (WAV), Laplacian of Gaussian (LOG) and 64 bin discretization (BIN) (alone or in combinations) and several features from images were extracted. The prediction performance of model was checked using the area under the receiver operator characteristic (ROC) curve (AUC). Results showed a wide range of radiomic model AUC performances up to 0.75 in prediction of EGFR and KRAS mutation status. Combination of K-Best and variance threshold feature selector with logistic regression (LREG) classifier in diagnostic CT scan led to the best performance in EGFR (CTD-BIN+B-KB+LREG, AUC: 0.75±0.10) and KRAS (CTD-BIN-LOG-WAV+B-VT+LREG, AUC: 0.75±0.07) respectively. Additionally, incorporating PET, kept AUC values at ~0.74. When considering conventional features only, highest predictive performance was achieved by PET SUVpeak (AUC: 0.69) for EGFR and by PET MTV (AUC: 0.55) for KRAS. In comparison with conventional PET parameters such as standard uptake value, radiomic models were found as more predictive. Our findings demonstrated that non-invasive and reliable radiomics analysis can be successfully used to predict EGFR and KRAS mutation status in NSCLC patients. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

From FAIR to CURE: Guidelines for Computational Models of Biological Systems

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

关键词：

Stabilizer slicing: Coherent error cancellations in LDPC codes

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Debroy, Dripto M. Li, Muyuan Newman, Michael Brown, Kenneth R. Department of Physics Duke University DurhamNC27708 United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA30332 United States Departments of Electrical and Computer Engineering and Chemistry Duke University DurhamNC27708 United States

Coherent errors are a dominant noise process in many quantum computing architectures. Unlike stochastic errors, these errors can combine constructively and grow into highly detrimental overrotations. To combat this, we introduce a simple technique for suppressing systematic coherent errors in low-density parity-check (LDPC) stabilizer codes, which we call stabilizer slicing. The essential idea is to slice low-weight stabilizers into two equally-weighted Pauli operators and then apply them by rotating in opposite directions, causing their overrotations to interfere destructively on the logical subspace. With access to native gates generated by 3-body Hamiltonians, we can completely eliminate purely coherent overrotation errors, and for overrotation noise of 0.99 unitarity we achieve a 135-fold improvement in the logical error rate of Surface-17. For more conventional 2-body ion trap gates, we observe an 89-fold improvement for Bacon-Shor-13 with purely coherent errors which should be testable in near-term fault-tolerance experiments. This second scheme takes advantage of the prepared gauge degrees of freedom, and to our knowledge is the first example in which the state of the gauge directly affects the robustness of a code's memory. This work demonstrates that coherent noise is preferable to stochastic noise within certain code and gate implementations when the coherence is utilized effectively. Copyright © 2018, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)