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Quantum error mitigation for rotation-symmetric bosonic codes with symmetry expansion

Physical Review A 2025年第6期111卷 062402-062402页

作者： Suguru Endo Yasunari Suzuki Kento Tsubouchi Rui Asaoka Kaoru Yamamoto Yuichiro Matsuzaki Yuuki Tokunaga NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Tokyo Japan Department of Applied Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) Central2 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan

The rotation symmetric bosonic code (RSBC) is a unified framework of practical bosonic codes that have rotation symmetries, such as cat codes and binomial codes. While cat and binomial codes achieve the breakeven point in which the coherence time of the encoded qubits exceeds that of unencoded qubits, the state preparation fidelity needs to be improved for practical quantum computing. Concerning this problem, we investigate the framework of symmetry expansion, a class of quantum error mitigation that virtually projects the state onto the noise-free symmetric subspace by exploiting the system's intrinsic symmetries and postprocessing of measurement outcomes. Although symmetry expansion has been limited to error mitigation of quantum states immediately before measurement, we successfully generalize symmetry expansion for state preparation. Then, we consider two types of stabilization: photon number and phase stabilization. Photon number stabilization can be performed by leveraging the rotation operators. On the other hand, the phase errors can be suppressed via the extension towards phase direction by leveraging the recently proposed projective squeezing method. To implement our method, we use an ancilla qubit and a randomly generated controlled gate between the bosonic code states and the ancilla qubit. Our novel error mitigation method will significantly enhance computation accuracy in the near-term bosonic quantum computing.

关键词： Qubits

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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The DECam Ecliptic Exploration Project (DEEP) IV: Constraints on the shape distribution of bright TNOs

arXiv

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arXiv 2023年

作者： Strauss, Ryder Trilling, David E. Bernardinelli, Pedro H. Beach, Christiano Oldroyd, William J. Sheppard, Scott S. Schlichting, Hilke E. Gerdes, David W. Adams, Fred C. Chandler, Colin Orion Fuentes, Cesar Holman, Matthew J. Jurić, Mario Lin, Hsing Wen Markwardt, Larissa McNeill, Andrew Mommert, Michael Napier, Kevin J. Payne, Matthew J. Ragozzine, Darin Rivkin, Andrew S. Smotherman, Hayden Trujillo, Chadwick A. Department of Astronomy and Planetary Science Northern Arizona University PO Box 6010 FlagstaffAZ86011 United States DiRAC Institute the Department of Astronomy University of Washington Seattle United States Department of Physics University of Michigan Ann ArborMI48109 United States Earth and Planets Laboratory Carnegie Institution for Science WashingtonDC20015 United States Department of Earth Planetary and Space Sciences University of California Los Angeles 595 Charles E. Young Dr. East Los AngelesCA90095 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Avenue TucsonAZ85721 United States Departamento de Astronomía Universidad de Chile Camino del Observatorio 1515 Las Condes Santiago Chile Harvard-Smithsonian Center for Astrophysics 60 Garden St. MS 51 CambridgeMA02138 United States Department of Physics Lehigh University 16 Memorial Drive East BethlehemPA18015 United States School of Computer Science University of St. Gallen Rosenbergstrasse 30 St. GallenCH-9000 Switzerland Department of Physics and Astronomy Brigham Young University ProvoUT84602 United States Applied Physics Lab Johns Hopkins University 11100 Johns Hopkins Road LaurelMD20723 United States

We present the methods and results from the discovery and photometric measurement of 26 bright (VR > 24 trans-Neptunian objects (TNOs) during the first year (2019-20) of the DECam Ecliptic Exploration Project (DEEP). The DEEP survey is an observational TNO survey with wide sky coverage, high sensitivity, and a fast photometric cadence. We apply a computer vision technique known as a progressive probabilistic Hough transform to identify linearly-moving transient sources within DEEP photometric catalogs. After subsequent visual vetting, we provide a photometric and astrometric catalog of our TNOs. By modeling the partial lightcurve amplitude distribution of the DEEP TNOs using Monte Carlo techniques, we find our data to be most consistent with an average TNO axis ratio b/a © 2023, CC BY.

关键词： Solar system

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Blockchain enabled smart contract based applications: Deficiencies with the software development life cycle models

arXiv

引用

arXiv 2020年

作者： Miraz, Mahdi H. Ali, Maaruf Centre for Financial Regulation and Economic Development Chinese University of Hong Kong Sha Tin Hong Kong School of Applied Science Computing and Engineering Wrexham Glyndŵr University Wrexham United Kingdom Department of Computer Engineering Epoka University Tirana Albania

With the recent popularity of Blockchain and other Distributed Ledger Technologies (DLT), blockchain enabled smart contract applications has attracted increased research focus. However, the immutability of the blocks, where the smart contracts are stored, causes conflicts with the traditional Software Development Life Cycle (SDLC) models usually followed by software engineers. This clearly shows the unsuitability of the application of SDLC in designing blockchain enabled smart contract based applications. This research article addresses this current problem by first exploring the six traditional SDLC models, clearly identifying the conflicts in a table with the application of smart contracts and advocates that there is an urgent need to develop new standard model(s) to address the arising issues. The concept of both block immutability and contract is introduced. This is further set in a historical context from legacy smart contracts and blockchain enabled smart contracts extending to the difference between "shallow smart contracts" and "deep smart contracts". To conclude, the traditional SDLC models are unsuitable for blockchain enabled smart contract-based applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity

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Scientific Reports 2025年第1期15卷 1-13页

作者： Bhandari, Shailendra Lencastre, Pedro Mathema, Rujeena Szorkovszky, Alexander Yazidi, Anis Lind, Pedro G. Department of Computer Science OsloMet – Oslo Metropolitan University P.O. Box 4 St. Olavs plass Oslo N-0130 Norway OsloMet Artificial Intelligence Lab Pilestredet 52 Oslo N-0166 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo 0164 Norway Department of Informatics University of Oslo Oslo Norway School of Economics Innovation and Technology Kristiania University of Applied Sciences Kirkegata 24-26 Oslo N-0153 Norway

Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a Generative Adversarial Network (GAN) framework employing Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM–trained under two conditions: using only adversarial loss () and using a weighted combination of adversarial and spectral losses. Our findings reveal that the LSTM-CNN architecture trained with this new loss function exhibits the closest alignment to the real data distribution, effectively capturing both the distribution tails and the intricate temporal dependencies. The inclusion of spectral regularization significantly enhances the GANs’ ability to replicate the spectral characteristics of eye gaze movements, leading to a more stable learning process and improved data fidelity. Comparative analysis with a Hidden Markov Model (HMM) optimized to four hidden states further highlights the advantages of the LSTM-CNN GAN. Statistical metrics show that the HMM-generated data significantly diverges from the real data in terms of mean, standard deviation, skewness, and kurtosis. In contrast, the LSTM-CNN model closely matches the real data across these statistics, affirming its capacity to model the complexity of eye gaze dynamics effectively. These results position the spectrally regularized LSTM-CNN GAN as a robust tool for generating synthetic eye gaze velocity data with high fidelity. Its ability to accurately replicate both the distributional and temporal properties of real data holds significant potent

关键词： Eye-gaze trajectories Generative Adversarial Networks Hidden Markov Models Stochastic Processes

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IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques

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Computational intelligence and neuroscience 2023年第1期2023卷 5113417页

作者： Pradeep Kumar Tiwari Pooja Singh Navaneetha Krishnan Rajagopal K Deepa Sampada Gulavani Amit Verma Yekula Prasanna Kumar Birla Global University Gothapatna Bhubaneswar Odisha India. School of Computing Science & Engineering Department of CSE Galgotias University Greater Noida UP India. Business Studies The University of Technology and Applied Sciences Salalah Oman. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thalavapalayam Karur Tamilnadu India. Department of MCA Bharati Vidyapeeth (Deemed to Be University) Institute of Management Kolhapur Maharashtra India. University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan Mohali Punjab India. Department of Mining Engineering College of Engineering and Technology Bule Hora University Blue Hora 144 Oromia Region Ethiopia.

computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is combined with IoT. The reinforcement learning techniques created in recent years have largely focused on incorporating deep learning technology to improve the generalization skills of the algorithm while ignoring the issue of detecting and taking full advantage of the dilemma. To increase the effectiveness of exploration, a deep reinforcement algorithm based on computational intelligence is proposed in this study, using intelligent sensors and the Bayesian approach. In addition, the technique for computing the posterior distribution of parameters in Bayesian linear regression is expanded to nonlinear models such as artificial neural networks. The Bayesian Bootstrap Deep -Network (BBDQN) algorithm is created by combining the bootstrapped DQN with the recommended computing technique. Finally, tests in two scenarios demonstrate that, when faced with severe exploration problems, BBDQN outperforms DQN and bootstrapped DQN in terms of exploration efficiency.

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Stagnation point flow of g-Jitter nanofluid induced by natural convection with Newtonian heating thermal condition

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AIP Conference Proceedings 2024年第1期2895卷

作者： Mohamad Hidayad Ahmad Kamal Anati Ali Noraihan Afiqah Rawi Lim Yeou Jiann Sharidan Shafie Rahimah Mahat Abdul Rahman Mohd Kasim Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 UTM Johor Bahru Johor Bahru Johor Darul Takzim Malaysia Faculty of Computer and Mathematical Science UiTM Cawangan Negeri Sembilan Kampus Seremban 70300 Seremban Negeri Sembilan Malaysia Universiti Kuala Lumpur Malaysian Institute of Industrial Technology Persiaran Sinaran Ilmu Bandar Seri Alam 81750 Johor Bahru Malaysia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Kuantan Pahang Malaysia

Understanding engine lubrication, electronic device cooling systems, and chemical industry production processes require studying boundary layer flow with Newtonian heating boundary conditions with varied consequences. The optimal performance of practical applications necessitates the proper conditions where many practical engineering applications using Newtonian heating in their theoretical study, such as in heat absorption in solar radiation, and conjugate heat transference around fins. As a result, theoretical research on free convection flow at a stagnation point region subjected to time dependent was examined utilizing mathematical modeling of the fluid system guided by physical laws and principles. The effects considered and fluid system configuration were also emphasized in developing the mathematical model. The study aimed to investigate the impacts included in the flow problem when subjected to Newtonian boundary conditions mathematically. The fluid problem’s system of partial differential equations was simplified using a semi-similar transformation, and it was then numerically solved using the Keller-box approach. The effects considered were critically analyzed in terms of profiles and physical quantities to understand the fluid behavior and thermal characteristics. The results revealed that adding nanoparticles to the fluid system improved the thermal characteristics of the fluid system by increasing the Nusselt number (Nu). Skin friction coefficient increases along with the nanoparticle volume fraction parameter. The skin friction coefficient increases as a result of increased friction at the boundary body’s surface caused by the nanoparticle silt.

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An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms

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Adsorption science & Technology 2022年 2022卷

作者： Anwer Mustafa Hilal Maha M. Althobaiti Taiseer Abdalla Elfadil Eisa Rana Alabdan Manar Ahmed Hamza Abdelwahed Motwakel Mesfer Al Duhayyim Noha Negm Department of Computer and Self Development Preparatory Year Deanship Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Science College of Computing and Information Technology Taif University P.O.Box 11099 Taif 21944 Saudi Arabia Department of Information Systems-Girls Section College of Science & Arts at Mahayil King Khalid University Saudi Arabia Department of Information Systems College of Computer and Information Sciences Majmaah University Majmaah 11952 Saudi Arabia Department of Natural and Applied Sciences College of Community-Aflaj Prince Sattam Bin Abdulaziz University Saudi Arabia Faculty of Science Mathematics and Computer Science Department Menoufia University Egypt

Purification of polluted water and return back to the agriculture field is the wastewater treatment for plants. Contaminated water causes illness and health emergencies of public. Also, health risk due release of toxic contaminants brings problem to all living beings. At present, sensors are used in waste water treatment and transfer data via internet of things (IoT). Prediction of wastewater quality content which is presence of total nitrogen (T-N) and total phosphorous (T-P) elements, chemical oxygen demand (COD), biochemical demand (BOD), and total suspended solids (TSS) is associated with eutrophication that should be prevented. This may leads to algal bloom and spoils aquatic life which is consumed by human. The presence of nitrogen and phosphorous elements is in the content of wastewater, and these elements are associated with eutrophication which should be prevented. Adsorption of T-N and T-P activated carbon was predictable as one of the most promising methods for wastewater treatment. Many research works have been done. The issues are inefficiency in the prediction of wastewater treatment. To overcome this issue, this paper proposed fusion of B-KNN with the ELM algorithm that is used. The accuracy of the BKNN-ELM algorithm in classification of water quality status produced the highest accuracy of the highest accuracy which is and with rate of accuracy which is 93.56%, and the lowest accuracy is . Experiment evaluation shows that a total suspended solid predicted by proposed model is 91 with accuracy of 93%. The relative error rate of prediction is 12.03 which is lesser than existing models.

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On the filtered polynomial interpolation at Chebyshev nodes

arXiv

引用

arXiv 2020年

作者： Occorsio, Donatella Themistoclakis, Woula Department of Mathematics Computer Science and Economics University of Basilicata Via dell’Ateneo Lucano 10 Potenza85100 Italy National Research Council of Italy IAC Institute for Applied Computing "Mauro Picone" Via P. Castellino 111 Napoli80131 Italy

The paper deals with a special filtered approximation method, which originates interpolation polynomials at Chebyshev zeros by using de la Vallée Poussin filters. These polynomials can be an useful device for many theoretical and applicative problems since they combine the advantages of the classical Lagrange interpolation, with the uniform convergence in spaces of locally continuous functions equipped with suitable, Jacobi–weighted, uniform norms. The uniform boundedness of the related Lebesgue constants, which equals to the uniform convergence and is missing from Lagrange interpolation, has been already proved in literature under different, but only sufficient, assumptions. Here, we state the necessary and sufficient conditions to get it. These conditions are easy to check since they are simple inequalities on the exponents of the Jacobi weight defining the norm. Moreover, they are necessary and sufficient to get filtered interpolating polynomials with a near best approximation error, which tends to zero as the number n of nodes tends to infinity. In addition, the convergence rate is comparable with the error of best polynomial approximation of degree n, hence the approximation order improves with the smoothness of the sought function. Several numerical experiments are given in order to test the theoretical results, to make a comparison with the Lagrange interpolation at the same nodes and to show how the Gibbs phenomenon can be strongly *** Codes 41A10, 65D05, 33C45 Copyright © 2020, The Authors. All rights reserved.

关键词： Lagrange multipliers

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A systematic approach to featurization for cancer drug sensitivity predictions with deep learning

arXiv

引用

arXiv 2020年

作者： Clyde, Austin Brettin, Tom Partin, Alexander Shaulik, Maulik Yoo, Hyunseung Evrard, Yvonne Zhu, Yitan Xia, Fangfang Stevens, Rick Department of Computer Science University of Chicago ChicagoIL United States Computing Environment and Life Sciences Argonne National Laboratory LemontIL United States Applied Development and Research Directorate Frederick National Laboratory for Cancer Research FrederickMD United States

By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions. In this paper we train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features. We found the inclusion of single nucleotide polymorphisms (SNPs) coded as count matrices improved model performance significantly, and no substantial difference in model performance with respect to molecular featurization between the common open source MOrdred descriptors and Dragon7 descriptors. Alongside this analysis, we outline data integration between CCL screening datasets and present evidence that new metrics and imbalanced data techniques, as well as advances in data standardization, need to be developed. Copyright © 2020, The Authors. All rights reserved.

关键词： Data integration

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