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Classifying extended,localized and critical states in quasiperiodic lattices via unsupervised learning

Chinese physics B 2025年第1期34卷 422-427页

作者： Bohan Zheng Siyu Zhu Xingping Zhou Tong Liu School of Computer Science and Technology School of SoftwareNanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Department of Applied Physics School of ScienceNanjing University of Posts and TelecommunicationsNanjing 210003China

Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave *** unsupervised learning results match well with those obtained through traditional numerical ***,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.

关键词： quantum phase quasiperiodic machine learning

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Machine learning sparse tight-binding parameters for defects

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npj Computational Materials 2022年第1期8卷 1081-1091页

作者： Christoph Schattauer Milica Todorović Kunal Ghosh Patrick Rinke Florian Libisch Institute for Theoretical Physics TU Wien1040ViennaAustria Department of Applied Physics Aalto UniversityP.O.Box 11100AaltoFI-00076Finland Department of Mechanical and Materials Engineering University of TurkuFI-20014TurkuFinland Department of Information and Computer Science Aalto UniversityP.O.Box 15400AaltoFI-00076Finland

We employ machine learning to derive tight-binding parametrizations for the electronic structure of *** test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding *** Multi-layer perceptrons(i.e.,feed-forward neural networks)perform best we adopt them for our further *** demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure,local density of states,transport and level spacing simulations for two common defects in single layer *** machine learning approach achieves results comparable to maximally localized Wannier functions(i.e.,DFT accuracy)without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation *** is general and can be applied to a wide range of other materials,enabling accurate large-scale simulations of material properties in the presence of different defects.

关键词： structure electronic spacing

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Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes 38

Trading Place for Space: Increasing Location Resolution Redu...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Rooke, Spencer Wang, Zhaoze Di Tullio, Ronald W. Balasubramanian, Vijay Departments of Physics University of Pennsylvania United States Departments of Computer and Information Science University of Pennsylvania United States Departments of Neuroscience University of Pennsylvania United States Rudolf Peierls Centre for Theoretical Physics University of Oxford United Kingdom Santa Fe Institute United States

Many animals learn cognitive maps of their environment - a simultaneous representation of context, experience, and position. Place cells in the hippocampus, named for their explicit encoding of position, are believed to be a neural substrate of these maps, with place cell "remapping" explaining how this system can represent different contexts. Briefly, place cells alter their firing properties, or "remap", in response to changes in experiential or sensory cues. Substantial sensory changes, produced, e.g., by moving between environments, cause large subpopulations of place cells to change their tuning entirely. While many studies have looked at the physiological basis of remapping, we lack explicit calculations of how the contextual capacity of the place cell system changes as a function of place field firing properties. Here, we propose a geometric approach to understanding population level activity of place cells. Using known firing field statistics, we investigate how changes to place cell firing properties affect the distances between representations of different environments within firing rate space. Using this approach, we find that the number of contexts storable by the hippocampus grows exponentially with the number of place cells, and calculate this exponent for environments of different sizes. We identify a fundamental trade-off between high resolution encoding of position and the number of storable contexts. This trade-off is tuned by place cell width, which might explain the change in firing field scale along the dorsal-ventral axis of the hippocampus. We demonstrate that clustering of place cells near likely points of confusion, such as boundaries, increases the contextual capacity of the place system within our framework and conclude by discussing how our geometric approach could be extended to include other cell types and abstract spaces. © 2024 Neural information processing systems foundation. All rights reserved.

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Scalable Quantum Photonic Neural Network Processor via Cavity-Assisted Interactions

Scalable Quantum Photonic Neural Network Processor via Cavit...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Basani, Jasvith Raj Niu, Murphy Yuezhen Waks, Edo Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States Department of Computer Science Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States

We present an integrated photonic architecture that uses a single atom trapped in a cavity for deterministic high-fidelity quantum operations. Our design is unique in providing a photon-number-selective nonlinearity, ... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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Machine learning models for modeling the biosorption of Fe(III) ions by activated carbon from olive stone

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Neural Computing and Applications 2024年第21期36卷 13357-13372页

作者： Massaoudi, Ayman Echouchene, Fraj Ben Ayed, Mossaad Berguiga, Abdelwahed Harchay, Ahlem Al-Ghamdi, Sara Belmabrouk, Hafedh Department of Computer Science College of Science and Arts in Gurayat Jouf University Sakakah Saudi Arabia Olive Research Center Jouf University Sakakah Saudi Arabia Laboratory of Electronics and Microelectronics Faculty of Science of Monastir University of Monastir Environment Boulevard Monastir5019 Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Department of Industrial Electronics ENISo University of Sousse Sousse Tunisia Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Physics College of Science Majmaah University Al Majma’ah11952 Saudi Arabia

Using experimental results related to the biosorption of Fe(III) by activated carbon derived from olive pit waste, we developed and evaluated four artificial neural network (ANN) models in this study, namely MLP-ANN, RBF-ANN, GR-ANN, and PSO-ANN, to predict the removal efficiency of Fe(III) during the adsorption process. The purpose of these models was to forecast the effect of the following five important operational variables: the initial concentration, time, stirring speed, temperature, and biosorbent dose. We conducted a thorough assessment of the performance of these models and compared their ability to predict the removal capacity. Several statistical metrics have been used to quantify the quality of the different models. The results calculated by the machine learning models were analyzed and compared with the experimental results. The obtained values of the coefficient of determination were 0.9997 for the PSO-ANN model, 0.991 for the GR-ANN model, 0.983 for the RBF-ANN model, and 0.837 for the MLP-ANN model concerning the removal efficiency. All the studied models are able to accurately predict the adsorbed quantity. ANOVA analysis was used to evaluate the effect of the parameters inherent to the PSO-ANN model. The PSO-ANN model proves to be a powerful tool for estimating the efficiency of Fe(III) ion removal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. corrected publication 2024.

关键词： Biosorption

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural Computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Distributed entanglement generation from asynchronously excited qubits

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Frontiers of physics 2022年第4期17卷 107-113页

作者： Tian-Tian Huan Rigui Zhou Hou Ian Institute of Applied Physics and Materials Engineering University of MacaoMacaoChina College of Mathematics and Computer Science Chifeng UniversityChifeng 024000China College of Information Engineering Shanghai Maritime UniversityShanghai 201306China

The generation of GHZ states calls for simultaneous excitation of multiple *** peculiarity of such states is reflected in their nonzero distributed entanglement which is not contained in other entangled *** study the optimal way to excite three superconducting qubits through a common cavity resonator in a circuit such that the generation of distributed entanglement among them could be obtained at the highest degree in a time-controllable way.A non-negative measure quantifying this entanglement is derived as a time function of the quadripartite system *** find that this measure does not stay static but obtains the same maximum *** the qubit-resonator couplings are allowed to vary,its peak value is enhanced monotonically by increasing the greatest coupling strength to one of the *** period of its peak to peak revival maximizes when the couplings become inhomogeneous,thus qubit excitation becoming asynchronous,at a relative ratio of *** study demonstrates the role of asynchronous excitations for time-controlling multi-qubit systems,in particular in extending entanglement time.

关键词： dynamic entanglement asynchronous excitation

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Strong nonlinear optical processes with extraordinary polarization anisotropy in inversionsymmetry broken two-dimensional PdPSe

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Light(science & Applications) 2024年第8期13卷 1524-1533页

作者： Song Zhu Ruihuan Duan Xiaodong Xu Fangyuan Sun Wenduo Chen Fakun Wang Siyuan Li Ming Ye Xin Zhou Jinluo Cheng Yao Wu Houkun Liang Junichiro Kono Xingji Li Zheng Liu Qi Jie Wang School of Electrical and Electronic Engineering Nanyang Technological University639798 SingaporeSingapore School of Material Science and Engineering Nanyang Technological University639798 SingaporeSingapore CINTRA CNRS/NTU/THALES UMI 3288Research Techno PlazaNanyang Technological University637371 SingaporeSingapore School of Materials Science and Engineering Harbin Institute of Technology150001 HarbinChina Department of Chemistry National University of Singapore117543 SingaporeSingapore GPL Photonics Lab State Key Laboratory of Applied OpticsChangchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of Sciences130033 ChangchunChina School of Electronics and Information Engineering Sichuan University610064 ChengduSichuanChina School of Physical and Mathematical Sciences Nanyang Technological University637371 SingaporeSingapore Departments of Electrical and Computer Engineering Physics and Astronomyand Materials Science and NanoEngineeringRice UniversityHoustonTXUSA

Nonlinear optical activities,especially second harmonic generation(SHG),are key phenomena in inversion-symmetrybroken two-dimensional(2D)transition metal dichalcogenides(TMDCs).On the other hand,anisotropic nonlinear optical processes are important for unique applications in nano-nonlinear photonic devices with polarization functions,having become one of focused research topics in the field of nonlinear ***,the strong nonlinearity and strong optical anisotropy do not exist simultaneously in common 2D ***,we demonstrate strong secondorder and third-order susceptibilities of 64 pm/V and 6.2×10^(-19)m^(2)/V^(2),respectively,in the even-layer PdPSe,which has not been discovered in other common TMDCs(e.g.,MoS2).Strikingly,it also simultaneously exhibited strong SHG anisotropy with an anisotropic ratio of~45,which is the largest reported among all 2D materials to date,to the best of our *** addition,the SHG anisotropy ratio can be harnessed from 0.12 to 45(375 times)by varying the excitation wavelength due to the dispersion ofχ^(2)*** an illustrative example,we further demonstrate polarized SHG imaging for potential applications in crystal orientation identification and polarization-dependent spatial *** findings in 2D PdPSe are promising for nonlinear nanophotonic and optoelectronic applications.

关键词： materials anisotropy nonlinear

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Trading place for space: increasing location resolution reduces contextual capacity in hippocampal codes 24

Trading place for space: increasing location resolution redu...

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Proceedings of the 38th International Conference on Neural information Processing Systems

作者： Spencer Rooke Zhaoze Wang Ronald W. Di Tullio Vijay Balasubramanian Departments of Physics University of Pennsylvania Departments of Computer and Information Science University of Pennsylvania Departments of Neuroscience University of Pennsylvania Departments of Physics University of Pennsylvania and Rudolf Peierls Centre for Theoretical Physics University of Oxford and Santa Fe Institute

ISBN: (纸本)9798331314385

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All-photonic artificial-neural-network processor via nonlinear optics

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Physical Review applied 2024年第1期22卷 014009-014009页

作者： Jasvith Raj Basani Mikkel Heuck Dirk R. Englund Stefan Krastanov Department of Electrical and Electronics Engineering Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics and Department of Electrical Engineering and Computer Science Department of Electrical and Photonics Engineering College of Information and Computer Sciences

Optics and photonics have recently captured interest as a platform to accelerate linear matrix processing, otherwise a bottleneck in traditional digital electronics. In this paper we propose an all-photonic computational accelerator wherein information is encoded in the amplitudes of frequency modes stored in a single ring resonator. Interaction among these modes is enabled by nonlinear optical processes. Both the matrix multiplication and elementwise activation functions on these modes (the artificial neurons) are performed through coherent processes, enabling the direct representation of negative and complex numbers without having to pass through digital electronics, a common limitation in today’s photonic architectures. This design also has a drastically lower hardware footprint compared with today’s electronic and optical accelerators, as the entirety of the matrix multiplication happens in a single multimode resonator on chip. Our architecture is unique in providing a completely unitary, reversible mode of computation, enabling on-chip analog Hamiltonian-echo backpropagation for gradient descent and other self-learning tasks. Moreover, the computational speed increases with the power of the pumps to arbitrarily high rates, as long as the circuitry can sustain the higher optical power. Lastly, the design presented here is a less demanding version of a future room-temperature quantum computational device. Therefore, while this architecture is already viable today, direct reinvestments in it would be enabling its evolution into quantum computational hardware.

关键词： Machine learning Neuromorphic computing Nonlinear optics Photonics Artificial neural networks Deep learning Neural network simulations Optical computing

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