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Non-Gaussian noise spectroscopy with a superconducting qubit sensor

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arXiv 2019年

作者： Sung, Youngkyu Beaudoin, Felix Norris, Leigh M. Yan, Fei Kim, David K. Qiu, Jack Y. Lüpke, Uwe Von Yoder, Jonilyn L. Orlando, Terry P. Gustavsson, Simon Viola, Lorenza Oliver, William D. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States MIT Lincoln Laboratory 244 Wood Street LexingtonMA02421 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Accurate characterization of the noise influencing a quantum system of interest has far-reaching implications across quantum science, ranging from microscopic modeling of decoherence dynamics to noise-optimized quantum control. While the assumption that noise obeys Gaussian statistics is commonly employed, noise is generically non-Gaussian in nature. In particular, the Gaussian approximation breaks down whenever a qubit is strongly coupled to discrete noise sources or has a non-linear response to the environmental degrees of freedom. Thus, in order to both scrutinize the applicability of the Gaussian assumption and capture distinctive non-Gaussian signatures, a tool for characterizing non-Gaussian noise is essential. Here, we experimentally validate a quantum control protocol which, in addition to the spectrum, reconstructs the leading higher-order spectrum of engineered non-Gaussian dephasing noise using a superconducting qubit as a sensor. This first experimental demonstration of non-Gaussian noise spectroscopy represents a major step toward demonstrating a complete spectral estimation toolbox for quantum devices. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

This looks like that: Deep learning for interpretable image recognition

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arXiv 2018年

作者： Chen, Chaofan Li, Oscar Tao, Chaofan Barnett, Alina Jade Su, Jonathan Rudin, Cynthia Department of Computer Science Duke University DurhamNC27708 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States MIT Lincoln Laboratory LexingtonMA02421-6426y United States

When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture that reasons in a similar way: The network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The model thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, geologists, architects, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training, meaning that there are no labels for parts of images. We demonstrate our method on the CUB-200-2011 dataset and the CBIS-DDSM dataset. Our experiments show that our interpretable network can achieve comparable accuracy with its analogous standard non-interpretable counterpart as well as other interpretable deep models. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification

Realizing the two-dimensional hard-core bose-hubbard model with superconducting qubits

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arXiv 2019年

作者： Yanay, Yariv Braumüller, Jochen Gustavsson, Simon Oliver, William D. Tahan, Charles Laboratory for Physical Sciences 8050 Greenmead Dr. College ParkMD20740 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States MIT Lincoln Laboratory 244 Wood Street LexingtonMA02421 United States Department of Electrical Engineering & Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

The pursuit of superconducting-based quantum computers has advanced the fabrication of and experimentation with custom lattices of qubits and resonators. Here, we describe a roadmap to use present experimental capabilities to simulate an interacting many-body system of bosons and measure quantities that are exponentially difficult to calculate numerically. We focus on the two-dimensional hard-core Bose-Hubbard model implemented as an array of floating transmon qubits. We describe a control scheme for such a lattice that can perform individual qubit readout and show how the scheme enables the preparation of a highly-excited many-body state, in contrast with atomic implementations restricted to the ground state or thermal equilibrium. We discuss what observables could be accessed and how they could be used to better understand the properties of many-body systems, including the observation of the transition of eigenstate entanglement entropy scaling from area law behavior to volume law behavior. Copyright © 2019, The Authors. All rights reserved.

关键词： Hubbard model

Flexible neural representation for physics prediction 18

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Flexible neural representation for physics prediction

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Damian Mrowca Chengxu Zhuang Elias Wang Nick Haber Li Fei-Fei Joshua B. Tenenbaum Daniel L. K. Yamins Department of Computer Science Stanford CA Department of Psychology Stanford CA Department of Electrical Engineering Stanford CA Department of Psychology Stanford CA and Department of Pediatrics Stanford CA and Department of Biomedical Data Sciencem Stanford CA Department of Brain and Cognitive Sciences MIT Cambridge MA and Department of Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA Department of Computer Science Stanford CA and Department of Psychology Stanford CA and Wu Tsai Neurosciences Institutem Stanford CA

Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of detail. Inspired by this ability, we propose a hierarchical particle-based object representation that covers a wide variety of types of three-dimensional objects, including both arbitrary rigid geometrical shapes and deformable materials. We then describe the Hierarchical Relation Network (HRN), an end-to-end differentiable neural network based on hierarchical graph convolution, that learns to predict physical dynamics in this representation. Compared to other neural network baselines, the HRN accurately handles complex collisions and nonrigid deformations, generating plausible dynamics predictions at long time scales in novel settings, and scaling to large scene configurations. These results demonstrate an architecture with the potential to form the basis of next-generation physics predictors for use in computer vision, robotics, and quantitative cognitive science.

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A pulsar-like swing in the polarisation position angle of a nearby fast radio burst

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arXiv 2024年

作者： McKinven, Ryan Bhardwaj, Mohit Eftekhari, Tarraneh Kilpatrick, Charles D. Kirichenko, Aida Pal, Arpan Cook, Amanda M. Gaensler, B.M. Giri, Utkarsh Kaspi, Victoria M. Michilli, Daniele Nimmo, Kenzie Pearlman, Aaron B. Pleunis, Ziggy Sand, Ketan R. Stairs, Ingrid Andersen, Bridget C. Andrew, Shion Bandura, Kevin Brar, Charanjot Cassanelli, Tomas Chatterjee, Shami Curtin, Alice P. Dong, Fengqiu Adam Eadie, Gwendolyn Fonseca, Emmanuel Ibik, Adaeze L. Kaczmarek, Jane F. Kharel, Bikash Lazda, Mattias Leung, Calvin Li, Dongzi Main, Robert Masui, Kiyoshi W. Mena-Parra, Juan Ng, Cherry Pandhi, Ayush Patil, Swarali Shivraj Prochaska, J. Xavier Rafiei-Ravandi, Masoud Scholz, Paul Shah, Vishwangi Shin, Kaitlyn Smith, Kendrick Department of Physics McGill University 3600 rue University MontréalQCH3A 2T8 Canada Trottier Space Institute McGill University 3550 rue University MontréalQCH3A 2A7 Canada McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Division of Physical and Biological Sciences University of California Santa Cruz Santa CruzCA95064 United States Department of Physics University of Wisconsin-Madison 1150 University Ave MadisonWI53706 United States Instituto de Astronomía Universidad Nacional Autónoma de México Apdo. Postal 877 Baja California Ensenada22800 Mexico MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States National Centre for Radio Astrophysics Tata Institute of Fundamental Research Pune411007 India Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Canada Lane Department of Computer Science and Electrical Engineering 1220 Evansdale Drive PO Box 6109 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8370451 Chile Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Statistical Sciences University of Toronto 700 University Av

Fast radio bursts (FRBs) last for milliseconds and arrive at Earth from cosmological distances. While their origin(s) and emission mechanism(s) are presently unknown, their signals bear similarities with the much less luminous radio emission generated by pulsars within our Galaxy1 and several lines of evidence point toward neutron star origins2,3. For pulsars, the linear polarisation position angle (PA) often exhibits evolution over the pulse phase that is interpreted within a geometric framework known as the rotating vector model (RVM)4. Here, we report on a fast radio burst, FRB 20221022A, detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and localized to a nearby host galaxy (∼ 65 Mpc), MCG+14-02-011. This one-off FRB displays a ∼ 130 degree rotation of its PA over its ∼ 2.5 ms burst duration, closely resembling the "S"-shaped PA evolution commonly seen from pulsars and some radio magnetars. The PA evolution disfavours emission models involving shocks far from the source5–7 and instead suggests magnetospheric origins8–10 for this source which places the emission region close to the FRB central engine, echoing similar conclusions drawn from tempo-polarimetric studies of some repeating sources11,12. This FRB’s PA evolution is remarkably well-described by the RVM and, although we cannot determine the inclination and magnetic obliquity due to the unknown period/duty cycle of the source, we can dismiss extremely short-period pulsars (e.g., recycled millisecond pulsars) as potential progenitors. RVM-fitting appears to favour a source occupying a unique position in the period/duty cycle phase space that implies tight opening angles for the beamed emission, significantly reducing burst energy requirements of the source. © 2024, CC BY.

关键词： Pulsars

Review of metamaterial-inspired vacuum electron devices

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Review of metamaterial-inspired vacuum electron devices

International Conference on Vacuum Electronics

作者： Zhaoyun Duan Michael A. Shapiro Yubin Gong Edi Schamiloglu Nader Behdad John H. Booske B. N. Basu Richard J. Temkin Institute of High Energy Electronics University of Electronic Science and Technology of China (UESTC) Chengdu China Plasma Science and Fusion Center Massachusetts Institute of Technology (MIT) Cambridge Massachusetts USA Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico USA Department of Electrical and Computer-Engineering University of Wisconsin Madison Wisconsin USA Sir J. C. Bose School of Engineering Mankundu 712139 West Bengal India

ISBN: (纸本)9781538604564

Metamaterials are subwavelength structure material engineered to have some unique properties that are not found in nature, such as reversed Cherenkov radiation (RCR). Based on the RCR, some important research groups in the world have made great attempts to develop novel metamaterial-inspired vacuum electron devices. These research results clearly show that metamaterial-inspired vacuum electron devices have very remarkable advantages, such as small size, high power, high efficiency, and high gain. These brand new metamaterial-inspired devices have promising applications in radar, communications, electronic warfare, microwave heating, accelerators, imaging, and many other areas.

关键词： Radar imaging Electron devices Electronic warfare Microwave communication Microwave devices

Noninvasive assessment of jugular venous pressure via force-coupled single crystal ultrasound

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Noninvasive assessment of jugular venous pressure via force-...

作者： Singh, Robin Jaffe, Alex Frydman, Galit H. Najia, Mohamad Ali Toufic Wei, Zijun Yang, Jason Majmudar, Maulik D. Department of Mechanical Engineering Medical Electronic Device Realization Centre Institute of Medical Engineering and Science Massachusetts Institute of Technology United States Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Division of Comparative Medicine Department of Biological Engineering Massachusetts Institute of Technology United States Center for Surgery Innovation and Bioengineering Department of Surgery Massachusetts General Hospital United States Harvard-MIT Health Sciences and Technology United States Broad Institute of MIT and Harvard United States Department of Hematology/Oncology Boston Children's Hospital United States Department of Media Arts and Sciences Massachusetts Institute of Technology United States Healthcare Transformation Lab Division of Cardiology Department of Medicine Massachusetts General Hospital BostonMA02114-2696 United States

Objective: we have developed a handheld device for noninvasive quantitative assessment of jugular venous pressure (JVP). Methods: we used a single crystal ultrasound coupled to a force-sensing load cell to measure JVP based on the force necessary to collapse the internal jugular vein (IJV) walls. We used a gelatin-based model system of the IJV to test the ability of single crystal ultrasound to identify the IJV and verified the cross-sectional position and diameter of the vessels with conventional imaging ultrasound. We also tested our prototype device on healthy human volunteers. Results: experiments on model system demonstrated that vessel diameters determined with single crystal ultrasound were in close agreement with the diameters derived from conventional 2-D ultrasound. Proof-of-concept human experiments demonstrate that single crystal ultrasound can detect the IJV in basal and collapsed states, as compared to gold-standard sonography (insert stats). Assessment of JVP in human volunteers was physiologically consistent with and sensitive to postural changes (supine JVP 6.6 ± 2.4 mmHg;standing JVP 4.2 ± 1.9 mmHg (p © 1964-2012 IEEE.

关键词： Single crystals

A fast radio burst localized at detection to an edge-on galaxy using very-long-baseline interferometry

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arXiv 2023年

作者： Cassanelli, Tomas Leung, Calvin Sanghavi, Pranav Mena-Parra, Juan Cary, Savannah McKinven, Ryan Bhardwaj, Mohit Masui, Kiyoshi W. Michilli, Daniele Bandura, Kevin Chatterjee, Shami Peterson, Jeffrey B. Kaczmarek, Jane Rahman, Mubdi Shin, Kaitlyn Vanderlinde, Keith Berger, Sabrina Brar, Charanjot Boyle, P.J. Breitman, Daniela Chawla, Pragya Curtin, Alice P. Dobbs, Matt Dong, Fengqiu Adam Fonseca, Emmanuel Gaensler, B.M. Ibik, Adaeze Kaspi, Victoria M. Kholoud, Khairy Lanman, Adam E. Lazda, Mattias Lin, Hsiu-Hsien Luo, Jing Meyers, Bradley W. Milutinovic, Nikola Ng, Cherry Noble, Gavin Pearlman, Aaron B. Pen, Ue-Li Petroff, Emily Pleunis, Ziggy Quine, Brendan Rafiei-Ravandi, Masoud Renard, Andre Sand, Ketan R. Schoen, Eve Scholz, Paul Smith, Kendrick M. Stairs, Ingrid Tendulkar, Shriharsh P. David A. Dunlap Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8370451 Chile MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Lane Department of Computer Science and Electrical Engineering West Virginia University 1220 Evansdale Drive PO Box 6109 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics Yale University New HavenCT06520 United States Department of Astronomy Wellesley College 106 Central Street WellesleyMA02481 United States Department of Physics McGill University 3600 rue University MontréalQCH3A 2T8 Canada Trottier Space Institute McGill University 3550 rue University MontréalQCH3A 2A7 Canada Cornell Center for Astrophysics and Planetary Science IthacaNY14853 United States McWilliams Center for Cosmology Department of Physics Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Dominion Radio Astrophysical Observatory Herzberg Research Centre for Astronomy and Astrophysics National Research Council Canada PO Box 248 PentictonBCV2A 6J9 Canada Department of Computer Science Math Physics & Statistics University of British Columbia KelownaBCV1V 1V7 Canada CSIRO Space & Astronomy Parkes Observatory P.O. Box 276 ParkesNSW2870 Australia Sidrat Research 124 Merton Street Suite 507 TorontoONM4S 2Z2 Canada Scuola Normale Superiore PI Pisa56126 Italy Anton Pannekoek Institute for Astronomy Univers

Fast radio bursts (FRBs) are millisecond-duration radio transients whose origins remain unknown. Since the vast majority of bursts are one-off events, it is necessary to pinpoint FRBs precisely within their host galaxies at the time of detection. Here, we use two purpose-built outrigger telescopes to localize FRB 20210603A at the time of its detection by the Canadian Hydrogen Intensity Mapping Experiment (CHIME). Our VLBI stations localize the burst to a 0.2′′ × 2′′ final ellipse in the disk of its host galaxy SDSS J004105.82+211331.9. Spatially-resolved spectroscopic followup reveals recent star formation (Hα emission) on kiloparsec scales near the burst position. The excess DM is consistent with expectations from the nearly edge-on disk of the host galaxy, demonstrating the utility of FRBs as probes of the interstellar medium in distant galaxies. The excess DM, rotation measure, and scattering are consistent with expectations for a pulse traveling from deep within its host galactic plane, strengthening the link between the local environment of FRB 20210603A and the disk of its host galaxy. Finally, this technique demonstrates a way to overcome the trade-off between angular resolution and field of view in FRB instrumentation, paving the way towards plentiful and precise FRB localizations. © 2023, CC BY.

关键词： Interferometry

Common Limitations of Image Processing Metrics: A Picture Story

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging