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Luis Pedro Garcia-Pintos, Tom O'Leary, Tanmoy Biswas, Jacob Bringewatt, Lukasz Cincio, Lucas Brady, Yi-Kai Liu
A leading approach to algorithm design aims to minimize the number of operations in an algorithm's compilation. One intuitively expects that reducing the number of operations may decrease the chance of errors. This paradigm is particularly prevalent in
Sampling from the output distributions of quantum computations comprising only commuting gates, known as instantaneous quantum polynomial (IQP) computations, is believed to be intractable for classical computers, and hence this task has become a leading
Undesired coupling to the surrounding environment destroys long-range correlations on quantum processors and hinders the coherent evolution in the nominally available computational space. This noise is an outstanding challenge to leverage the computation
Jacob Collard, Valeria de Paiva, Eswaran Subrahmanian
Mathematics is a highly specialized domain with its own unique set of challenges. Despite this, there has been relatively little research on natural language processing for mathematical texts, and there are few mathematical language resources aimed at NLP
The National Institute of Standards and Technology (NIST) is evaluating and improving the specification for achieving interoperability of containerized computational software. Adherence to a specification for Findable, Accessible, Interoperable, and
We review the current status of efforts to develop and deploy post-quantum cryptography on the Internet. Then we suggest specific ways in which quantum technologies might be used to enhance cybersecurity in the near future and beyond. We focus on two goals
Jim Filliben has inspired many scientists in a variety of domains across the National Institute of Standard and Technology (NIST) with his statistical data analysis. In our talk we will highlight the impact of his insights and methodologies on our projects
Kamal Choudhary, Francesca Tavazza, Carelyn E. Campbell, Vishu Gupta, Yuwei Mao, Kewei Wang, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available
Recent advances in first principles calculations and machine learning techniques allow a systematic search for phonon-mediated superconductors. We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen
Miranda Mundt, Jonathan Bisila, Jonathan E. Guyer, Daniel Howard, Daniel S. Katz, Reed Milewicz, Henry Schreiner, Joshua Teves, Chris Wiswell
The explosion of Research Software Engineers (RSEs) in the United States created the opportunity to form communities of practice (CoP), groups which share a passion for an activity and learn how to do it better as they interact regularly, specifically to
Simulation is a useful and effective way to analyze and study complex, real-world systems. It allows researchers, practitioners, and decision makers to make sense of the inner working of a system that involves many factors often resulting in some sort of
Peter Bajcsy, Daniel Gao, Michael Paul Majurski, Thomas Cleveland, Manuel Carrasco, Michael Buschmann, Walid Keyrouz
With the widespread creation of artificial intelligence (AI) models in biosciences, bio-medical researchers are reusing trained AI models from other applications. This work is motivated by the need to characterize trained AI models for reuse based on
Jacob Collard, Valeria de Paiva, Brendan Fong, Eswaran Subrahmanian
We investigate some different systems for extracting mathematical entities from texts in the mathematical field of category theory, as a first step for constructing a mathematical knowledge graph. We consider four different term extractors and compare
Cassandra Pegg, Benjamin Schulz, Ben Neely, Gregory Albery, Colin Carlson
The sugars that coat the outsides of viruses and host cells are key to successful disease transmission, but they remain understudied compared to other molecular features. Understanding the comparative zoology of glycosylation - and harnessing it for
Naohiro Fujinuma, Brian DeCost, Jason Hattrick-Simpers, Sam Lofland
Applied machine learning has rapidly spread throughout the physical sciences. In fact, machine learning-based data analysis and experimental decision-making have become commonplace. Here, we reflect on the ongoing shift in the conversation from proving
A barrier to developing novel AI for complex reasoning is the lack of appropriate wargaming platforms for training and evaluating AIs in a multiplayer setting combining collaborative and adversarial reasoning under uncertainty with game theory and
Logan Howe, Manuel Castellanos Beltran, Adam Sirois, David Olaya, John Biesecker, Paul Dresselhaus, Samuel P. Benz, Pete Hopkins
Scaling of quantum computers to fault-tolerant levels relies critically on the integration of energy-efficient, stable, and reproducible qubit control and readout electronics. In comparison to traditional semiconductor-control electronics (TSCE) located at
Zhengwei Wang, Kevin O'Dwyer, Ryan Muddiman, Tomas Ward, Charles Camp, Bryan Hennelly
Rapid label-free spectroscopy of biological and chemical specimen via molecular vibration through means of Broadband Coherent Anti-Stokes Raman Scattering (B-CARS) could serve as a basis for a robust diagnostic platform for a wide range of applications. A
Peter Bajcsy, Mathias Hammer, Maximiliaan Huisman, Alex Rigano, Ulrike Boehm, James J. Chambers, Nathalie Gaudreault, Jaime A. Pimentel, Damir Sudar, Claire M. Brown, Alexander D. Corbett, Orestis Faklaris, Judith Lacoste, Alex Laude, Glyn Nelson, Roland Nitschke, Alison J. North, Renu Gopinathan, Farzin Farzam, Carlas Smith, David Grunwald, Caterina Strambio-De-Castillia
While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be properly interpreted (quality), reproduced (reproducibility), and used to extract reliable
Finite element meshes constructed from 3D images are useful in materials science and medical applications when it is necessary to model the actual geometry of a sample, rather than an idealized approximation of it. Constructing the mesh involves computing
Mathieu Riou, Jacob Torrejon, Flavio Abreu Araujo, Sumito Tsunegi, Guru Khalsa, Damien Querlioz, Paolo Bortolotti, Nathan Leroux, Danijela Markovic, Vincent Cros, K. Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Mark D. Stiles, Julie Grollier
Present artificial intelligence algorithms require extensive computations to emulate the behavior of large neural networks, operating current computers near their limits, which leads to high energy costs. A possible solution to this problem is the
Peter Bajcsy, Mylene Simon, Sunny Yu, Nick Schaub, Jayapriya Nagarajan, Sudharsan Prativadi, Mohamed Ouladi, Nathan Hotaling
Microscopy image-based measurement variability in high-throughput imaging experiments for biological drug discoveries, such as COVID-19 therapies was addressed in this study. Variability of measurements came from (1) computational approaches (methods), (2)
We present a new collection of processing techniques, collectively "factorized Kramers-Kroenig and error correction" (fKK-EC), for (a) Raman signal extraction, (b) denoising, and (c) phase- and scale- error correction in coherent anti-Stokes Raman
Race logic, an arrival-time-coded logic family, has demonstrated energy and performance improvements for a subset of applications ranging from dynamic programming to machine learning. In the absence of memory, or a mathematical framework for abstraction