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Hamiltonian engineering with constrained optimization for quantum sensing and control

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arXiv 2018年

作者： O'Keeffe, Michael F. Horesh, Lior Barry, John F. Braje, Danielle A. Chuang, Isaac L. Quantum Information and Integrated Nanosystems Group MIT Lincoln Laboratory LexingtonMA02421 United States AI Science IBM T.J. Watson Research Center Yorktown HeightsNY10598 United States Department of Physics Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States MIT-IBM Watson AI Lab CambridgeMA02142 United States

While quantum devices rely on interactions between constituent subsystems and with their environment to operate, native interactions alone often fail to deliver targeted performance. Coherent pulsed control provides the ability to tailor effective interactions, known as Hamiltonian engineering. We propose a Hamiltonian engineering method that maximizes desired interactions while mitigating deleterious ones by conducting a pulse sequence search using constrained optimization. The optimization formulation incorporates pulse sequence length and cardinality penalties consistent with linear or integer programming. We apply the general technique to magnetometry with solid state spin ensembles in which inhomogeneous interactions between sensing spins limit coherence. Defining figures of merit for broadband Ramsey magnetometry, we present novel pulse sequences which outperform known techniques for homonuclear spin decoupling in both spin-1/2 and spin-1 systems. When applied to nitrogen vacancy (NV) centers in diamond, this scheme partially preserves the Zeeman interaction while zeroing dipolar coupling between negatively charged NV-centers. Such a scheme is of interest for NV- magnetometers which have reached the NV--NV- coupling limit. We discuss experimental implementation in NV ensembles, as well as applicability of the current approach to more general spin bath decoupling and superconducting qubit control. Copyright © 2018, The Authors. All rights reserved.

关键词： Hamiltonians

Universal research index: An inclusive metric to quantify scientific research output

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Journal of Academic Librarianship 2023年第3期49卷

作者： Keshavarz-Fathi, Mahsa Yazdanpanah, Niloufar Kolahchi, Sajad Ziaei, Heliya Darmstadt, Gary L. Dorigo, Tommaso Dochy, Filip Levin, Lisa Thongboonkerd, Visith Ogino, Shuji Chen, Wei-Hsin Perc, Matjaz Tremblay, Mark S. Olusanya, Bolajoko O. Rao, Idupulapati M. Hatziargyriou, Nikos Moradi-Lakeh, Maziar Bella, Federico Rosivall, Laszlo Gandomi, Amir H. Sorooshian, Armin Gupta, Manoj Gal, Ciprian Lozano, Andres M. Weaver, Connie Tanzer, Michael Poggi, Alessandro Sepanlou, Sadaf G. Weiskirchen, Ralf Jambrak, Anet Režek Torres, Pedro J. Capanoglu, Esra Barba, Francisco J. Ernest, Chua Kian Jon Sigman, Mariano Pluchino, Stefano Gharehpetian, Gevork B. Fereshtehnejad, Seyed-Mohammad Yang, Muh-Hwa Thomas, Sabu Cai, Wenju Comini, Elisabetta Scolding, Neil J. Myles, Paul S. Nieto, Juan J. Perry, George Sedikides, Constantine Rezaei, Nima School of Medicine Tehran University of Medical Sciences Tehran Iran Universal Scientific Education and Research Network (USERN) Iran Department of Pediatrics Stanford University School of Medicine Stanford CA United States Istituto Nazionale di Fisica Nucleare (INFN) Via Francesco Marzolo Sezione di Padova Italy High Impact Learning Academy Learning and Development Department Brussels Belgium Center for Marine Biodiversity and Conservation Integrative Oceanography Division Scripps Institution of Oceanography University of California San Diego San Diego CA United States Medical Proteomics Unit Office for Research and Development Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Program in MPE Molecular Pathological Epidemiology Department of Pathology Brigham and Women's Hospital and Harvard Medical School Boston MA United States Broad Institute of MIT and Harvard Cambridge MA United States Department of Aeronautics and Astronautics National Cheng Kung University Tainan Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung 407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung 411 Taiwan Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Complexity Science Hub Vienna Vienna Austria Alma Mater Europaea Maribor Slovenia Children's Hospital of Eastern Ontario Research Institute Department of Pediatrics University of Ottawa Ottawa ON Canada Centre for Healthy Start Initiative Ikoyi Lagos Nigeria Alliance of Bioversity International International Center for Tropical Agriculture Cali Colombia International Centre of Insect Physiology and Ecology (ICIPE) Nairobi Kenya School of Electrical and Computer Engineering Natio

Scientometrics and bibliometrics, the subfields of library and information science, deal with the quantity and quality of research outputs. Currently, various scientometric indices are being used to quantify and compare research outputs. The most widely known is the h-index. However, this index and its derivatives suffer from dependence on the mere count of a scholar's highly cited publications. To remedy this deficiency, we developed a novel index, the Universal Research Index (UR-Index) (https://***/UR-Index/) by which every single publication has its own impact on the total score. We developed this index by surveying international top 1 % cited scientists in various disciplines and included additional component variables such as publication type, leading role of a scholar, co-author count, and source metrics to this scientometric index. We acknowledge that unconscious biases built into the component variables included in the UR-Index might put research from specific groups at a disadvantage, thus continued efforts to improve equitable scholarly impact in science and academia are encouraged. © 2023 Elsevier Inc.

关键词： Citations CiteScore h-index Impact factor Leading author Research impact Scholarly impact Scholarly output Scientometrics UR-Index USERN

Semi-transparent solar cell enabled by frequency selective light trapping

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arXiv 2017年

作者： Wheeler, Duncan C. Shen, Yichen Yang, Yi Boriskina, Svetlana V. Huang, Yi Ilic, Ognjen Chen, Gang Soljačić, Marin Department of Physics MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Mechanical Engineering MIT United States Department of Applied Physics Caltech United States

We propose a frequency selective light trapping scheme that enables the creation of more visually-transparent and yet simultaneously more efficient semitransparent solar cells. A nanoparticle scattering layer and photonic stack back reflector create a selective trapping effect by total internal reflection within a medium, increasing absorption of IR light. We propose a strong frequency selective scattering layer using spherical TiO2 nanoparticles with radius of 255 nm and area density of 1.1% in a medium with index of refraction of 1.5. Using detailed numerical simulations for this configuration, we find that it is possible to create a semitransparent silicon solar cell that has a Shockley Queisser efficiency of 12.0%±0.4% with a visible transparency of 60.2%±1.3%, 13.3%±1.3% more visibly-transparent than a bare silicon cell at the same efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Refractive index

Automated discovery of superconducting circuits and its application to 4-local coupler design

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arXiv 2019年

作者： Menke, Tim Häse, Florian Gustavsson, Simon Kerman, Andrew J. Oliver, William D. Aspuru-Guzik, Alán Department of Physics Harvard University CambridgeMA02138 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemistry and Chemical Biology Harvard University CambridgeMA02138 United States Department of Chemistry Department of Computer Science University of Toronto TorontoONM5S 3H6 Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada MIT Lincoln Laboratory 244 Wood Street LexingtonMA02420 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Lebovic Fellow TorontoONM5S 1M1 Canada

Superconducting circuits have emerged as a promising platform to build quantum processors. The challenge of designing a circuit is to compromise between realizing a set of performance metrics and reducing circuit complexity and noise sensitivity. At the same time, one needs to explore a large design space, and computational approaches often yield long simulation times. Here we automate the circuit design task using SCILLA, a software for automated discovery of superconducting circuits. SCILLA performs a parallelized, closed-loop optimization to design circuit diagrams that match predefined properties such as spectral features and noise sensitivities. We employ it to discover 4-local couplers for superconducting flux qubits and identify a circuit that outperforms an existing proposal with similar circuit structure in terms of coupling strength and noise resilience for experimentally accessible parameters. This work demonstrates how automated discovery can facilitate the design of complex circuit architectures for quantum information processing. Copyright © 2019, The Authors. All rights reserved.

关键词： Timing circuits

Inflammation Biomarkers: Label-Free Biophysical Markers from Whole Blood Microfluidic Immune Profiling Reveal Severe Immune Response Signatures (Small 12/2021)

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Small 2021年第12期17卷

作者： Kerwin Kwek Zeming Rohan Vernekar Mui Teng Chua Kai Yun Quek Greg Sutton Timm Krüger Win Sen Kuan Jongyoon Han Singapore-MIT Alliance for Research and Technology (SMART) – Critical Analytics for Manufacturing of Personalized Medicine (CAMP) IRG 1 Create Way Enterprise Wing #04-13/14 Singapore 138602 Singapore School of Engineering Institute for Multiscale Thermofluids University of Edinburgh Peter Guthrie Tait Road King's Buildings Edinburgh EH9 3FD UK Department of Surgery Yong Loo Lin School of Medicine National University of Singapore 1E Kent Ridge Road Level 8 NUHS Tower Block Singapore 119228 Singapore Emergency Medicine Department National University Hospital National University Health System National University Centre for Oral Health 9 Lower Kent Ridge Road Level 4 Singapore 119085 Singapore University/BHF Centre for Cardiovascular Science The Queen's Medical Research Institute 47 Little France Crescent Edinburgh EH16 4TJ UK Department of Electrical Engineering and Computer Science & Department of Biological Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Room 36–841 Cambridge MA 02139 USA

A novel discriminative feature extraction for acoustic scene classification using RNN based source separation

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A novel discriminative feature extraction for acoustic scene...

作者： Mun, Seongkyu Shon, Suwon Kim, Wooil Han, David K. Ko, Hanseok Department of Visual Information Processing Korea University Seoul136–713 Korea Republic of MIT CambridgeMA United States Dept. of Computer Science and Engineering Incheon National University Incheon Korea Republic of School of Electrical Engineering Korea University Seoul136–713 Korea Republic of

Various types of classifiers and feature extraction methods for acoustic scene classification have been recently proposed in the IEEE Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Challenge Task 1. The results of the final evaluation, however, have shown that even top 10 ranked teams, showed extremely low accuracy performance in particular class pairs with similar sounds. Due to such sound classes being difficult to distinguish even by human ears, the conventional deep learning based feature extraction methods, as used by most DCASE participating teams, are considered facing performance limitations. To address the low performance problem in similar class pair cases, this letter proposes to employ a recurrent neural network (RNN) based source separation for each class prior to the classification step. Based on the fact that the system can effectively extract trained sound components using the RNN structure, the mid-layer of the RNN can be considered to capture discriminative information of the trained class. Therefore, this letter proposes to use this mid-layer information as novel discriminative features. The proposed feature shows an average classification rate improvement of 2.3% compared to the conventional method, which uses additional classifiers for the similar class pair issue. Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.

关键词： Recurrent neural networks

Development of quantum InterConnects (QuICs) for next-generation information technologies

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arXiv 2019年

作者： Awschalom, David Berggren, Karl K. Bernien, Hannes Bhave, Sunil Carr, Lincoln D. Davids, Paul Economou, Sophia E. Englund, Dirk Faraon, Andrei Fejer, Marty Guha, Saikat Gustafsson, Martin V. Hu, Evelyn Jiang, Liang Kim, Jungsang Korzh, Boris Kumar, Prem Kwiat, Paul G. Lončar, Marko Lukin, Mikhail D. Miller, David A.B. Monroe, Christopher Nam, Sae Woo Narang, Prineha Orcutt, Jason S. Raymer, Michael G. Safavi-Naeini, Amir H. Spiropulu, Maria Srinivasan, Kartik Sun, Shuo Vučković, Jelena Waks, Edo Walsworth, Ronald Weiner, Andrew M. Zhang, Zheshen Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Physics Colorado School of Mines 1500 Illinois St. GoldenCO80401 United States Photonic & Phononic Microsystems Sandia National Laboratory AlbuquerqueNM87185 United States Department of Physics Virginia Tech BlacksburgVA24061 United States T .J. Watson Laboratory of Applied Physics California Institute of Technology PasadenaCA91125 United States Kavli Nanoscience Institute California Institute of Technology PasadenaCA91125 United States E.L. Ginzton Laboratory Stanford University StanfordCA94305 United States College of Optical Sciences University of Arizona TucsonAZ85721 United States Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States Department of Applied Mathematics University of Arizona TucsonAZ85721 United States Raytheon BBN Technologies CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Harvard University CambridgeMA02138 United States Department Of Electrical and Computer Engineering Duke University DurhamNC27708 United States IonQ Inc. College ParkMD20740 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States IQUIST University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics Harvard University Cambridge

Just as ‘classical’ information technology rests on a foundation built of interconnected information-processing systems, quantum information technology (QIT) must do the same. A critical component of such systems is the ‘interconnect,’ a device or process that allows transfer of information between disparate physical media, for example, semiconductor electronics, individual atoms, light pulses in optical fiber, or microwave fields. While interconnects have been well engineered for decades in the realm of classical information technology, quantum interconnects (QuICs) present special challenges, as they must allow the transfer of fragile quantum states between different physical parts or degrees of freedom of the system. The diversity of QIT platforms (superconducting, atomic, solid-state color center, optical, etc.) that will form a ‘quantum internet’ poses additional challenges. As quantum systems scale to larger size, the quantum interconnect bottleneck is imminent, and is emerging as a grand challenge for QIT. For these reasons, it is the position of the community represented by participants of the NSF workshop on "Quantum Interconnects" that accelerating QuIC research is crucial for sustained development of a national quantum science and technology program. Given the diversity of QIT platforms, materials used, applications, and infrastructure required, a convergent research program including partnership between academia, industry and national laboratories is required. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum optics

Tissue engineering: Controlled Apoptosis of Stromal Cells to Engineer Human Microlivers (Adv. Funct. Mater. 48/2020)

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Advanced Functional Materials 2020年第48期30卷

作者： Amanda X. Chen Arnav Chhabra Hyun‐Ho Greco Song Heather E. Fleming Christopher S. Chen Sangeeta N. Bhatia Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA 02139 USA Harvard–MIT Program in Health Sciences and Technology Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA Biological Design Center Department of Biomedical Engineering Boston University Boston MA 02215 USA Wyss Institute for Biologically Inspired Engineering Boston MA 02115 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02142 USA Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA 02115 USA Broad Institute of Massachusetts Institute of Technology and Harvard Cambridge MA 02139 USA Howard Hughes Medical Institute Chevy Chase MD 20815 USA

Testing bounded arboricity

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arXiv 2017年

作者： Eden, Talya Levi, Reut Ron, Dana CSAIL at MIT Efi Arazi School of Computer Science Interdisciplinary Center Israel School of Electrical Engineering Tel Aviv University

In this paper we consider the problem of testing whether a graph has bounded arboricity. The class of graphs with bounded arboricity includes many important graph families (e.g., planar graphs and randomly generated preferential attachment graphs). Graphs with bounded arboricity have been studied extensively in the past, in particular, since for many problems they allow for much more efficient algorithms and/or better approximation ratios. We present a tolerant tester in the general-graphs model. The general-graphs model allows access to degree and neighbor queries, and the distance is defined with respect to the actual number of edges. Namely, we say that a graph G is ǫ-close to having arboricity α, if by removing at most an ǫ-fraction of its edges we can obtain a graph G′ that has arboricity α, and otherwise we say that G is ǫ-far. Our algorithm distinguishes between graphs that are ǫ-close to having arboricity α and graphs that are c·ǫ-far from having arboricity 3α, where c is an absolute small constant. The query complexity and running time of the algorithm are Õ ( ǫ√nm ) +(1ǫ )O(log(1/ǫ)) where n denotes the number of vertices and m denotes the number of edges (we use the notation Õ to hide poly-logarithmic factors in n). In terms of the dependence on n and m this bound is optimal up to poly-logarithmic factors since Ω (√nm ) queries are necessary. Copyright © 2017, The Authors. All rights reserved.

关键词： Graphic methods