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Quantum approximation of normalized Schatten norms and applications to learning

Physical Review A 2022年第5期106卷 052409-052409页

作者： Yiyou Chen Hideyuki Miyahara Louis-S. Bouchard Vwani Roychowdhury Department of Computer Science University of California Los Angeles California 90095 USA Department of Electrical and Computer Engineering University of California Los Angeles California 90095 USA Department of Chemistry and Biochemistry University of California Los Angeles California 90095 USA Department of Bioengineering University of California Los Angeles California 90095 USA Center for Quantum Science and Engineering University of California Los Angeles California 90095 USA California NanoSystems Institute University of California Los Angeles California 90095 USA The Molecular Biology Institute University of California Los Angeles California 90095 USA

Efficient measures to determine the similarity of quantum states, such as the fidelity metric, have been widely studied. In this paper, we address the problem of defining a similarity measure for quantum operations that can be efficiently estimated. Given two quantum operations, U1 and U2, represented in their circuit forms, we first develop a quantum sampling circuit to estimate the normalized Schatten 2-norm of their difference (∥U1−U2∥S2) with precision ε, using only one clean qubit and one classical random variable. We prove a Poly(1ε) upper bound on the sample complexity, which is independent of the size of the quantum system. We then show that such a similarity metric is directly related to a functional definition of similarity of unitary operations using the conventional fidelity metric of quantum states (F): If ∥U1−U2∥S2 is sufficiently small (e.g., ≤ε1+2(1/δ−1)) then the fidelity of states obtained by processing the same randomly and uniformly picked pure state |ψ〉 is as high as needed [F(U1|ψ〉,U2|ψ〉)≥1−ε] with probability exceeding 1−δ. We provide example applications of this efficient similarity metric estimation framework to quantum circuit learning tasks, such as finding the square root of a given unitary operation.

关键词： Machine learning Quantum algorithms Quantum computation

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Architectures for Heterogeneous Quantum Error Correction Codes

arXiv

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arXiv 2024年

作者： Stein, Samuel Xu, Shifan Cross, Andrew W. Yoder, Theodore J. Javadi-Abhari, Ali Liu, Chenxu Liu, Kun Zhou, Zeyuan Guinn, Charles Ding, Yufei Ding, Yongshan Li, Ang Pacific Northwest National Laboratory RichlandWA United States Departments of Applied Physics Yale University New HavenCT United States Yale Quantum Institute Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY United States Department of Physics Princeton University PrincetonNJ United States Department of Computer Science & Engineering University of Califronia San Diego San DiegoCA United States Department of Electrical & Computer Engineering University of Washington WashingtonWA United States

Quantum Error Correction (QEC) is essential for future quantum computers due to its ability to exponentially suppress physical errors. The surface code is a leading error-correcting code candidate because of its local topological structure, experimentally achievable thresholds, and support for universal gate operations with magic states. However, its physical overhead scales quadratically with number of correctable errors. Conversely, quantum low-density parity-check (qLDPC) codes offer superior scaling but lack, on their own, a clear path to universal logical computation. Therefore, it is becoming increasingly evident that there are significant advantages to designing architectures using multiple codes. Heterogeneous architectures provide a clear path to universal logical computation as well as the ability to access different resource trade offs. To address this, we propose integrating the surface code and gross code using an ancilla bus for inter-code data movement. This approach involves managing trade-offs, including qubit overhead, a constrained instruction set, and gross code (memory) routing and management. While our focus is on the gross-surface code architecture, our method is adaptable to any code combination and the constraints generated by that specific architecture. Motivated by the potential reduction of physical qubit overhead, an ever important feature in the realization of fault tolerant computation, we perform the first full system study of heterogeneous error-correcting codes, discovering architectural trade-offs and optimizing around them. We demonstrate physical qubit reductions of up to 6.42× when executing an algorithm to a specific logical error rate, at the cost of up to a 3.43× increase in execution time. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Photonic Crystal Enhanced Fluorescence with DNA-based Nano-gripper for Ultrasensitive Intact Viral Pathogen Biosensing 22

Photonic Crystal Enhanced Fluorescence with DNA-based Nano-g...

引用

Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XXII 2025

作者： Xiong, Yanyu Zhou, Lifeng Cooper, Laura Zheng, Mengxi Shepherd, Skye Dwivedy, Abhisek Song, Tingjie Hong, Wei Chen, Xin Liu, Shengyan Le, Linh T.P. Umrao, Saurabh Rong, Lijun Wang, Tong Wang, Xing Cunningham, Brian T. Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Microbiology and Immunology College of Medicine University of Illinois at Chicago ChicagoIL60612 United States Department of Chemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign United States Advanced Science Research Center at Graduate Center City University of New York New YorkNY10031 United States Cancer Center at Illinois University of Illinois at Urbana-Champaign UrbanaIL61801 United States

ISBN: (纸本)9781510684188

We introduce a novel biosensing approach that utilizes photonic crystal-enhanced fluorescence technology integrated with a bioinspired DNA origami NanoGripper, selectively generating a fluorescent signal only upon detecting intact viral pathogens. Using intact SARS-CoV-2 as a representative example of a respiratory virus, this method demonstrates rapid, ultrasensitive, and highly selective biosensing. Photonic crystals (PC), serving as dielectric microcavities, provide substantial local field enhancement, far-field directional emission, large Purcell factors, and high quantum efficiency. These novel origami probes are coupled with a Photonic Crystal Enhanced Fluorescence Microscope, achieving a 10,000-fold signal enhancement compared to traditional single fluorophore reporters on glass substrates. Unlike surface-based ELISA assays, the capture reaction between the NanoGripper and the SARS-CoV-2 virus occurs in solution, reaching equilibrium within 10 minutes. After mixing, the mixture is pulled down to the PC surface for signal enhancement and direct counting. With virtually zero off-target signals, we achieve a limit of detection (LoD) of 100 viral genome copies per mL in human saliva, enabling ultra-high detection sensitivity. © 2025 SPIE.

关键词： Coronavirus

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Bifunctional Noble Metal-free Ternary Chalcogenide Electrocatalysts for Overall Water Splitting

arXiv

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arXiv 2024年

作者： Singh, Shantanu Irshad, Ahamed De la Cruz, Germany Diaz Zhao, Boyang Zayat, Billal Chang, Qiaowan Narayan, Sri Ravichandran, Jayakanth Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States Department of Chemistry University of Southern California Los AngelesCA90089 United States The Gene and Linda Voiland School of Chemical Engineering and Bioengineering Washington State University PullmanWA99164 United States Ming Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Core Center of Excellence in Nano Imaging University of Southern California Los AngelesCA90089 United States

Hydrogen has been identified as a clean, zero carbon, sustainable, and promising energy source for the future, and electrochemical water splitting for hydrogen production is an emission-free, efficient energy conversion technology. A major limitation of this approach is the unavailability of efficient, abundant, inexpensive catalysts, which prompts the need for new catalytic materials. Here, we report the synthesis and electrocatalytic properties of a novel transition metal-based ternary chalcogenide family, LaMS3 (M = Mn, Fe, Co, Ni). Powder X-ray diffraction confirms the phase purity of these materials, while composition analysis using energy dispersive spectroscopy (EDS) confirms the presence of the stoichiometric ratio of elements in these compounds. X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) were used to study the chemical states on the surface and in bulk, respectively. These materials exhibit bifunctional catalytic activity towards the two half-reactions of the water-splitting process, with LaNiS3 being the most active material for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The LaMS3 compounds show long-term stability with negligible change in the overpotential at a constant current density of 10 mA cm-2 over 18 hours of measurements. As compared to the corresponding ternary oxides, the LaMS3 materials exhibit higher activity and significantly lower Tafel slopes. The ability to catalyze both half-reactions of water electrolysis makes these materials promising candidates for bifunctional catalysts and presents a new avenue to search for high-efficiency electrocatalysts for water splitting. © 2024, CC BY.

关键词： Electrocatalysts

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DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization

arXiv

引用

arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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Endoscopic Fourier-transform infrared spectroscopy through a fiber microprobe

arXiv

引用

arXiv 2024年

作者： Kim, Jaehyeon Tian, Yue Qiao, Guanhua Villarta, Julinna Abulencia Zhao, Fujia He, Andrew Ho, Ruo-Jing Liu, Haoran Bhargava, Rohit Zhang, Yingjie Department of Materials Science and Engineering University of Illinois UrbanaIL61801 United States Materials Research Laboratory University of Illinois UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois UrbanaIL61801 United States Department of Bioengineering University of Illinois UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States Cancer Center at Illinois University of Illinois UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois UrbanaIL61801 United States Department of Chemical and Biomolecular Engineering University of Illinois UrbanaIL61801 United States Department of Chemistry University of Illinois UrbanaIL61801 United States

Fourier-transform infrared spectroscopy (FTIR) is a powerful analytical method for not only the chemical identification of solid, liquid, and gas species, but also the quantification of their concentration. However, the chemical quantification capability of FTIR is significantly hindered when the analyte is surrounded by a strong IR absorbing medium, such as liquid solutions. To overcome this limit, here we develop an IR fiber microprobe that can be inserted into liquid medium, and obtain full FTIR spectra at points of interest. To benchmark this endoscopic FTIR method, we insert the microprobe into bulk water covering a ZnSe substrate and measure the IR transmittance of water as a function of the probe–substrate distance. The obtained vibrational modes, overall transmittance vs z profiles, quantitative absorption coefficients, and micro z-section IR transmittance spectra are all consistent with the standard IR absorption properties of water. The results pave the way for endoscopic chemical profiling inside bulk liquid solutions, promising for applications in many biological, chemical, and electrochemical systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Fourier transform infrared spectroscopy

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Photochromic-Induced Photorefractive Changes in Barium Titanate

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MRS Online Proceedings Library 2020年第1期228卷 147-155页

作者： M. H. Garrett J. Y. Chang H. P. Jenssen C. Warde Center for Materials Science and Engineering Massachusetts Institute of Technology Cambridge Departments of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge

Photochromism in barium titanate was found to affect its deep and shallow trap properties. Argon ion laser illumination at 514.5 nm photo-induced semi-permanent (thermally reversible) changes in the visible absorption spectrum of barium titanate that were stable at room temperature. Increases in absorption at 514.5 nm, also the operating wavelength used to determine photorefractive properties, caused a decrease in the photorefractive response time but also a decrease in the sensitivity. When the photochromism was inactive the crystal shows light-induced absorption. With the photochromism saturated the crystal showed light-induced transparency. A model with two photoactive levels is used to characterize the photorefractive properties of this photochromic crystal.

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Hyperplastic and tubular polyp classification using machine learning and feature selection

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Intelligence-Based Medicine 2024年 10卷 100177-100177页

作者： Doğan, Refika Sultan Akay, Ebru Doğan, Serkan Yılmaz, Bülent Department of Bioengineering Faculty of Life and Natural Sciences Abdullah Gül University Kayseri 38080 Turkey Biomedical Instrumentation and Signal Analysis Laboratory Abdullah Gül University Kayseri 38080 Turkey Pathology Clinic Kayseri City Hospital Kayseri 38080 Turkey Gastroenterology Clinic Kayseri City Hospital Kayseri 38080 Turkey GUST Engineering and Applied Innovation Research Center (GEAR) Electrical and Computer Engineering Department Gulf University for Science and Technology (GUST) Hawally 32093 Kuwait

Purpose: The aim of this study is to develop an effective approach for differentiating between hyperplastic and tubular adenoma colon polyps, which is one of the most difficult tasks in colonoscopy procedures. The main research challenge is how to improve the classification of these polyp subtypes applying various focusing levels on the polyp images, data preprocessing approaches, and classification algorithms. Methods: This study employed 202 colonoscopy videos from a total of 201 patients, focusing on 59 videos containing hyperplastic and tubular adenoma polyps. Manually extract key frames and several feature extraction and classification techniques were applied. The influence of different datasets with various focuses as well as data preprocessing steps on the performance of classification was examined, and AUC values were calculated using ten classifiers. Results: The study discovered that the optimal dataset, data preprocessing method, and classification algorithm all had significant effects on classification results. The Random Forest model with the Recursive Feature Elimination (RFE) feature selection approach, for example, consistently outperformed other models and achieved the highest AUC value of 0.9067. In terms of accuracy, F1 score, recall, and AUC, the suggested model outperformed a gastroenterologist, nevertheless precision remained slightly lower. Conclusion: This study emphasizes the importance of dataset selection, data preprocessing, and feature selection in enhancing the classification of difficult colon polyp subtypes. The suggested model offers a promising model for the clinical differentiation of hyperplastic and tubular adenoma polyps, potentially improving diagnostic accuracy in gastroenterology. © 2024 The Authors

关键词： Classification Colonoscopy Colorectal polyps Feature extraction Feature selection Machine learning

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HOW DOES THE BRAIN COMPUTE WITH PROBABILITIES?

arXiv

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arXiv 2024年

作者： Haefner, Ralf M. Beck, Jeff Savin, Cristina Salmasi, Mehrdad Pitkow, Xaq Department of Brain and Cognitive Sciences University of Rochester RochesterNY United States Department of Neurobiology Duke University DurhamNC United States Departments of Neural Science and Data Science New York University New YorkNY United States Gatsby Computational Neuroscience Unit Max Planck UCL Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Neuroscience Institute Department of Machine Learning Carnegie Mellon University PittsburghPA United States Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering Department of Computer Science Rice University HoustonTX United States

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations - Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) - and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and *** Codes 92 Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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