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arXiv 2025年

作者： Liu, Minzhao Shaydulin, Ruslan Niroula, Pradeep DeCross, Matthew Hung, Shih-Han Kon, Wen Yu Cervero-Martín, Enrique Chakraborty, Kaushik Amer, Omar Aaronson, Scott Acharya, Atithi Alexeev, Yuri Berg, K. Jordan Chakrabarti, Shouvanik Curchod, Florian J. Dreiling, Joan M. Erickson, Neal Foltz, Cameron Foss-Feig, Michael Hayes, David Humble, Travis S. Kumar, Niraj Larson, Jeffrey Lykov, Danylo Mills, Michael Moses, Steven A. Neyenhuis, Brian Eloul, Shaltiel Siegfried, Peter Walker, James Lim, Charles Pistoia, Marco Global Technology Applied Research JPMorganChase New YorkNY10017 United States Quantinuum BroomfieldCO80021 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States Department of Physics The University of Chicago ChicagoIL60637 United States Department of Computer Science The University of Texas at Austin AustinTX78712 United States Department of Electrical Engineering National Taiwan University Taipei City10617 Taiwan Quantinuum Terrington House 13–15 Hills Road CambridgeCB2 1NL United Kingdom Quantum Science Center Oak Ridge National Laboratory Oak RidgeTN37831 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States

While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers [1, 2], realizing this potential remains a challenge. One such task is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy [3]. Certified randomness has many applications [4] but is fundamentally impossible to achieve solely by classical computation. In this work, we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations [5, 6]: a client generates quantum "challenge" circuits using a small randomness seed, sends them to an untrusted quantum server to execute, and verifies the server’s results. We analyze the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1 × 1018 floating-point operations per second across multiple supercomputers, we certify 71, 313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of today’s quantum computers. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum computers

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Layer-dependent optically induced spin polarization in InSe

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Physical Review B 2023年第11期107卷 115304-115304页

作者： Jovan Nelson Teodor K. Stanev Dmitry Lebedev Trevor LaMountain J. Tyler Gish Hongfei Zeng Hyeondeok Shin Olle Heinonen Kenji Watanabe Takashi Taniguchi Mark C. Hersam Nathaniel P. Stern Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Material Science and Engineering Northwestern University Evanston Illinois 60208 USA Computational Science Division Argonne National Laboratory Lemont Illinois 60439 USA Material Science Division Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

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 optoelectronics due to its high electron mobility, tunable direct band gap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules, suggesting the 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 photoluminescence 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 optospintronic 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.

关键词： Magneto-optics Spin dynamics Chalcogenides Layered semiconductors Van der Waals systems Density functional theory Kerr effect Photoluminescence k dot p method

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Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications

arXiv

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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Cajas, Sebastían Andrés Nakayama, Luis Filipe Celi, Leo Anthony López, Diego M. Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telemática Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States John A. Paulson School of Engineering and Applied Sciences Harvard University BostonMA United States School of Computer Science University College Dublin Belfield Dublin Ireland Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States

Objective: Large-scale multi-modal deep learning models and datasets have revolutionized various domains such as healthcare, underscoring the critical role of computational power. However, in resource-constrained regions like Low and Middle-Income Countries (LMICs), GPU and data access is limited, leaving many dependent solely on CPUs. To address this, we advocate leveraging vector embeddings for flexible and efficient computational methodologies, aiming to democratize multimodal deep learning across diverse contexts. Background and Significance: Our paper investigates the computational efficiency and effectiveness of leveraging vector embeddings, extracted from singlemodal foundation models and multi-modal Vision-Language Models (VLM), for multimodal deep learning in low-resource environments, particularly in healthcare applications. Additionally, we propose an easy but effective inference-time method to enhance performance by further aligning image-text embeddings. Materials and Methods: By comparing these approaches with traditional multimodal deep learning methods, we assess their impact on computational efficiency and model performance using accuracy, F1-score, inference time, training time, and memory usage across 3 medical modalities such as BRSET (ophthalmology), HAM10000 (dermatology), and SatelliteBench (public health). Results: Our findings indicate that embeddings reduce computational demands without compromising the model's performance, and show that our embedding alignment method improves the performance of the models in medical tasks. Discussion: This research contributes to sustainable AI practices by optimizing computational resources in resource-constrained environments. It highlights the potential of embedding-based approaches for efficient multimodal learning. Conclusion: Vector embeddings democratize multimodal deep learning in LMICs, especially in healthcare. Our study showcases their effectiveness, enhancing AI adaptability in varied use cases

关键词： Embeddings

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High-fidelity image reconstruction for compressed ultrafast photography via an augmented-Lagrangian and deep-learning hybrid algorithm

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Photonics Research 2021年第2期9卷 I0023-I0030页

作者： CHENGSHUAI YANG YUNHUA YAO CHENGZHI JIN DALONG QI FENGYAN CAO YILIN HE JIALI YAO PENGPENG DING LIANG GAO TIANQING JIA JINYANG LIANG ZHENRONG SUN SHIAN ZHANG State Key Laboratory of Precision Spectroscopy School of Physics and Electronic ScienceEast China Normal University Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Institut National de la Recherche Scientifique Centreénergie Matériaux TélécommunicationsLaboratory of Applied Computational Imaging Collaborative Innovation Center of Extreme Optics Shanxi University Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal University

Compressed ultrafast photography(CUP) is the fastest single-shot passive ultrafast optical imaging technique,which has shown to be a powerful tool in recording self-luminous or non-repeatable ultrafast ***, the low fidelity of image reconstruction based on the conventional augmented-Lagrangian(AL)and two-step iterative shrinkage/thresholding(Tw IST) algorithms greatly prevents practical applications of CUP, especially for those ultrafast phenomena that need high spatial resolution. Here, we develop a novel AL and deep-learning(DL) hybrid(i.e., AL+DL) algorithm to realize high-fidelity image reconstruction for CUP. The AL+DL algorithm not only optimizes the sparse domain and relevant iteration parameters via learning the dataset but also simplifies the mathematical architecture, so it greatly improves the image reconstruction accuracy. Our theoretical simulation and experimental results validate the superior performance of the AL+DL algorithm in image fidelity over conventional AL and Tw IST algorithms, where the peak signalto-noise ratio and structural similarity index can be increased at least by 4 d B(9 d B) and 0.1(0.05) for a complex(simple) dynamic scene, respectively. This study can promote the applications of CUP in related fields, and it will also enable a new strategy for recovering high-dimensional signals from low-dimensional detection.

关键词： image algorithm luminous

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FerroX: A GPU-accelerated, 3D Phase-Field Simulation Framework for Modeling Ferroelectric Devices

arXiv

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arXiv 2022年

作者： Kumar, Prabhat Nonaka, Andrew Jambunathan, Revathi Pahwa, Girish Salahuddin, Sayeef Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory CA94720 United States Berkeley Device Modeling Center University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States

We present a massively parallel, 3D phase-field simulation framework for modeling ferroelectric materials based scalable logic devices. This code package, FerroX, self-consistently solves the time-dependent Ginzburg Landau (TDGL) equation for ferroelectric polarization, Poisson’s equation for electric potential, and charge equation for carrier densities in semiconductor regions. The algorithm is implemented using the AMReX software framework 1, which provides effective scalability on manycore and GPU-based supercomputing architectures. We demonstrate the performance of the algorithm with excellent scaling results on NERSC multicore and GPU systems, with a significant (15x) speedup on the GPU using a node-by-node comparison. We further demonstrate the applicability of the code in simulations of ferroelectric domain-wall induced negative capacitance (NC) effect in Metal-Ferroelectric-Insulator-Metal (MFIM) and Metal-Ferroelectric-Insulator-Semiconductor-Metal (MFISM) devices. The charge (Q) v.s. voltage (V) responses for these 3D structures clearly indicates stabilized negative capacitance with multidomain formation, which is corroborated by amplification of the voltage at the interface between the ferroelectric and dielectric layers. © 2022, CC BY.

关键词： Electric potential

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Quantum Linear System Solvers: A Survey of Algorithms and Applications

arXiv

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arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

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Electron charge qubits with 0.1 millisecond coherence time

arXiv

引用

arXiv 2022年

作者： Zhou, Xianjing Li, Xinhao Chen, Qianfan Koolstra, Gerwin Yang, Ge Dizdar, Brennan Huang, Yizhong Wang, Christopher S. Han, Xu Zhang, Xufeng Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL60439 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States James Franck Institute Department of Physics University of Chicago ChicagoIL60637 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Department of Applied Physics Stanford University StanfordCA94305 United States Department of Physics and Astronomy University of Notre Dame Notre DameIN46556 United States

Electron charge qubits are compelling candidates for solid-state quantum computing because of their inherent simplicity in qubit design, fabrication, control, and readout. However, all existing electron charge qubits, built upon conventional semiconductors and superconductors, suffer from severe charge noise that limits the coherence time to the order of 1 microsecond. Here, we report our experimental realization of ultralongcoherence electron charge qubits, based upon isolated single electrons trapped on an ultraclean solid neon surface in vacuum. Quantum information is encoded in the motional states of an electron that is strongly coupled with microwave photons in an on-chip superconducting resonator. The measured relaxation time T1 and coherence time T2 are both on the order of 0.1 milliseconds. The single-shot readout fidelity without using a quantum-limited amplifier is 98.1%. The average single-qubit gate fidelity using Clifford-based randomized benchmarking is 99.97%. Simultaneous strong coupling of two qubits with the same resonator is demonstrated, as a first step toward two-qubit entangling gates for universal quantum computing. These results manifest that the electron-on-solid-neon (eNe) charge qubits outperform all existing charge qubits to date and rival state-of-the-art superconducting transmon qubits, offering an appealing platform for quantum computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Superconducting resonators

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Decoding the Functional Interactome of Non-model Organisms with PHILHARMONIC 29th

Decoding the Functional Interactome of Non-model Organisms...

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29th International Conference on Research in computational Molecular Biology, RECOMB 2025

作者： Sledzieski, Sam Versavel, Charlotte Singh, Rohit Ocitti, Faith Devkota, Kapil Kumar, Lokender Shpilker, Polina Roger, Liza Yang, Jinkyu Lewinski, Nastassja Putnam, Hollie Berger, Bonnie Klein-Seetharaman, Judith Cowen, Lenore Center for Computational Biology Flatiron Institute New YorkNY United States Department of Computer Science Tufts University MedfordMA United States Departments of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Biotechnology Faculty of Applied Sciences and Biotechnology Shoolini University Himachal Pradesh Solan173229 India School of Molecular Sciences and School of Ocean Futures Arizona State University TempeAZ United States Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Chemical and Life Science Engineering Virginia Commonwealth University RichmondVA United States Department of Biological Sciences University of Rhode Island KingstonRI United States Computer Science and Artificial Intelligence Laboratory and Department of Mathematics MIT CambridgeMA United States School of Molecular Sciences and College of Health Solutions Arizona State University PhoenixAZ United States

ISBN: (纸本)9783031902512

We introduce PHILHARMONIC, a computational framework that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our novel clustering approach produces highly informative functional modules by de-noising the predicted network. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We initially perform remote homology-based functional annotation by leveraging hmmscan and GODomainMiner to assign initial functions to proteins at large evolutionary distances;our clusters then enable us to newly assign functions to uncharacterized proteins through "function by association." We validate the ability of PHILHARMONIC to recover gold-standard functional enrichments in the well-annotated fruit fly D. melanogaster, and apply it to investigate stress response in the reef-building coral P. damicornis and its algal symbiont C. goreaui. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Qudit Dynamical Decoupling on a Superconducting Quantum Processor

arXiv

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arXiv 2024年

作者： Tripathi, Vinay Goss, Noah Vezvaee, Arian Nguyen, Long B. Siddiqi, Irfan Lidar, Daniel A. Department of Physics & Astronomy University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Applied Math and Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical & Computer Engineering University of Southern California Los AngelesCA90089 United States Department of Chemistry University of Southern California Los AngelesCA90089 United States

Multi-level qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoherence than qubits due to increased loss channels, noise sensitivity, and crosstalk. To address these challenges, we develop protocols for dynamical decoupling (DD) of qudit systems based on the Heisenberg-Weyl group. We implement and experimentally verify these DD protocols on a superconducting transmon processor that supports qudit operation based on qutrits (d = 3) and ququarts (d = 4). Specifically, we demonstrate single-qudit DD sequences to decouple qutrits and ququarts from system-bath-induced decoherence. We also introduce two-qudit DD sequences designed to suppress the detrimental cross-Kerr couplings between coupled qudits. This allows us to demonstrate a significant improvement in the fidelity of time-evolved qutrit Bell states. Our results highlight the utility of leveraging DD to enable scalable qudit-based quantum computing. © 2024, CC BY.

关键词： Qubits

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Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes

arXiv

引用

arXiv 2023年

作者： Mahbub, Maria Goethert, Ian Danciu, Ioana Knight, Kathryn Srinivasan, Sudarshan Tamang, Suzanne Rozenberg-Ben-Dror, Karine Solares, Hugo Martins, Susana Trafton, Jodie Begoli, Edmon Peterson, Gregory D. Cyber Resilience and Intelligence Division Oak Ridge National Laboratory Oak RidgeTN United States Information Technology Services Division Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Biomedical Informatics Vanderbilt University NashvilleTN United States Program Evaluation and Resource Center Office of Mental Health and Suicide Prevention Department of Veterans Affairs United States Department of Medicine Stanford University School of Medicine StanfordCA United States Veterans Affairs Great Lakes Health Care System WestchesterIL United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville KnoxvilleTN United States

Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a question-answering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care. © 2023, CC BY-NC-ND.

关键词： Data warehouses

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