Search Results - "Balewski, Jan"

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs by Balewski, Jan, Amankwah, Mercy G., Van Beeumen, Roel, Bethel, E. Wes, Perciano, Talita, Camps, Daan

“…Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two…”
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Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models by Ladd, Alexander, Kim, Kyung Geun, Balewski, Jan, Bouchard, Kristofer, Ben-Shalom, Roy

“…Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural…”
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Co-design Center for Exascale Machine Learning Technologies (ExaLearn) by Alexander, Francis J, Ang, James, Bilbrey, Jenna A, Balewski, Jan, Casey, Tiernan, Chard, Ryan, Choi, Jong, Choudhury, Sutanay, Debusschere, Bert, DeGennaro, Anthony M, Dryden, Nikoli, Ellis, J Austin, Foster, Ian, Cardona, Cristina Garcia, Ghosh, Sayan, Harrington, Peter, Huang, Yunzhi, Jha, Shantenu, Johnston, Travis, Kagawa, Ai, Kannan, Ramakrishnan, Kumar, Neeraj, Liu, Zhengchun, Maruyama, Naoya, Matsuoka, Satoshi, McCarthy, Erin, Mohd-Yusof, Jamaludin, Nugent, Peter, Oyama, Yosuke, Proffen, Thomas, Pugmire, David, Rajamanickam, Sivasankaran, Ramakrishniah, Vinay, Schram, Malachi, Seal, Sudip K, Sivaraman, Ganesh, Sweeney, Christine, Tan, Li, Thakur, Rajeev, Van Essen, Brian, Ward, Logan, Welch, Paul, Wolf, Michael, Xantheas, Sotiris S, Yager, Kevin G, Yoo, Shinjae, Yoon, Byung-Jun

“…Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML),…”
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QubiC: An Open-Source FPGA-Based Control and Measurement System for Superconducting Quantum Information Processors by Xu, Yilun, Huang, Gang, Balewski, Jan, Naik, Ravi, Morvan, Alexis, Mitchell, Bradley, Nowrouzi, Kasra, Santiago, David I., Siddiqi, Irfan

“…As quantum information processors grow in quantum bit (qubit) count and functionality, the control and measurement system becomes a limiting factor to…”
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Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes by Andreopoulos, William B, Geller, Alexander M, Lucke, Miriam, Balewski, Jan, Clum, Alicia, Ivanova, Natalia N, Levy, Asaf

“…Abstract Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such…”
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The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism by Oyama, Yosuke, Maruyama, Naoya, Dryden, Nikoli, McCarthy, Erin, Harrington, Peter, Balewski, Jan, Matsuoka, Satoshi, Nugent, Peter, Van Essen, Brian

“…We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows…”
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The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism by Oyama, Yosuke, Maruyama, Naoya, Dryden, Nikoli, Mccarthy, Erin, Harrington, Peter, Balewski, Jan, Matsuoka, Satoshi, Nugent, Peter, Van Essen, Brian

“…Here, we present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific…”
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Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data by Muszynski, Grzegorz, Prabhat, Balewski, Jan, Kashinath, Karthik, Wehner, Michael, Kurlin, Vitaliy

“…In this paper, we address a problem of atmospheric blocking pattern recognition in global climate model simulation data. Understanding blocking events is a…”
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Asynchronous I/O Strategy for Large-Scale Deep Learning Applications by Lee, Sunwoo, Kang, Qiao, Wang, Kewei, Balewski, Jan, Sim, Alex, Agrawal, Ankit, Choudhary, Alok, Nugent, Peter, Wu, Kesheng, Liao, Wei-keng

“…Many scientific applications have started using deep learning methods for their classification or regression problems. However, for data-intensive scientific…”
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Efficient Quantum Counting and Quantum Content-Addressable Memory for DNA Similarity by Balewski, Jan, Camps, Daan, Klymko, Katherine, Tritt, Andrew

“…We present QCAM, a quantum analogue of Content-Addressable Memory (CAM), useful for finding matches in two sequences of bit-strings. Our QCAM implementation…”
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Efficient Quantum Counting and Quantum Content-Addressable Memory for DNA similarity by Balewski, Jan, Camps, Daan, Klymko, Katherine, Tritt, Andrew

“…We present QCAM, a quantum analogue of Content-Addressable Memory (CAM), useful for finding matches in two sequences of bit-strings. Our QCAM implementation…”
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Engineering quantum states with neutral atoms by Balewski, Jan, Kornjača, Milan, Klymko, Katherine, Darbha, Siva, Hirsbrunner, Mark R, Lopes, Pedro L. S, Liu, Fangli, Camps, Daan

“…Aquila, an analog quantum simulation platform developed by QuEra Computing, supports control of the position and coherent evolution of up to 256 neutral atoms…”
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Quantum-parallel vectorized data encodings and computations on trapped-ions and transmons QPUs by Balewski, Jan, Amankwah, Mercy G, Van Beeumen, Roel, Bethel, E. Wes, Perciano, Talita, Camps, Daan

“…Compact quantum data representations are essential to the emerging field of quantum algorithms for data analysis. We introduce two new data encoding schemes,…”
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Quantum Computing and Visualization: A Disruptive Technological Change Ahead by Bethel, E. Wes, Amankwah, Mercy G, Balewski, Jan, Van Beeumen, Roel, Camps, Daan, Huang, Daniel, Perciano, Talita

“…The focus of this Visualization Viewpoints article is to provide some background on Quantum Computing (QC), to explore ideas related to how visualization helps…”
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Time-series ML-regression on Graphcore IPU-M2000 and Nvidia A100 by Balewski, Jan, Liu, Zhenying, Tsyplikhin, Alexander, Lopez Roland, Manuel, Bouchard, Kristofer

“…We compare the ML-training performance of a Graphcore IPU-M2000-based system with Nvidia A100 GPU-based system on the Perlmutter HPC machine at NERSC/LBL. The…”
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Long-lived oscillations of metastable states in neutral atom systems by Darbha, Siva, Kornjača, Milan, Liu, Fangli, Balewski, Jan, Hirsbrunner, Mark R, Lopes, Pedro L. S, Wang, Sheng-Tao, Van Beeumen, Roel, Klymko, Katherine, Camps, Daan

“…Phys. Rev. B 110, 155114 (2024) Metastable states arise in a range of quantum systems and can be observed in various dynamical scenarios, including decay,…”
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False vacuum decay and nucleation dynamics in neutral atom systems by Darbha, Siva, Kornjača, Milan, Liu, Fangli, Balewski, Jan, Hirsbrunner, Mark R, Lopes, Pedro L. S, Wang, Sheng-Tao, Van Beeumen, Roel, Camps, Daan, Klymko, Katherine

“…Phys. Rev. B 110, 155103 (2024) Metastable states of quantum many-body systems with confinement offer a means to simulate false vacuum phenomenology, including…”
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ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements by Vora, Neel R, Xu, Yilun, Hashim, Akel, Fruitwala, Neelay, Nguyen, Ho Nam, Liao, Haoran, Balewski, Jan, Rajagopala, Abhi, Nowrouzi, Kasra, Ji, Qing, Whaley, K. Birgitta, Siddiqi, Irfan, Nguyen, Phuc, Huang, Gang

“…Similar to reading the transistor state in classical computers, identifying the quantum bit (qubit) state is a fundamental operation to translate quantum…”
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MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems by Farrell, Steven, Emani, Murali, Balma, Jacob, Drescher, Lukas, Drozd, Aleksandr, Fink, Andreas, Fox, Geoffrey, Kanter, David, Kurth, Thorsten, Mattson, Peter, Mu, Dawei, Ruhela, Amit, Sato, Kento, Shirahata, Koichi, Tabaru, Tsuguchika, Tsaris, Aristeidis, Balewski, Jan, Cumming, Ben, Danjo, Takumi, Domke, Jens, Fukai, Takaaki, Fukumoto, Naoto, Fukushi, Tatsuya, Gerofi, Balazs, Honda, Takumi, Imamura, Toshiyuki, Kasagi, Akihiko, Kawakami, Kentaro, Kudo, Shuhei, Kuroda, Akiyoshi, Martinasso, Maxime, Matsuoka, Satoshi, Mendonca, Henrique, Minami, Kazuki, Ram, Prabhat, Sawada, Takashi, Shankar, Mallikarjun, John, Tom St, Tabuchi, Akihiro, Vishwanath, Venkatram, Wahib, Mohamed, Yamazaki, Masafumi, Yin, Junqi

“…Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High…”
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Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications by Wang, Kewei, Lee, Sunwoo, Balewski, Jan, Sim, Alex, Nugent, Peter, Agrawal, Ankit, Choudhary, Alok, Wu, Kesheng, Liao, Wei-Keng

“…Neural networks are powerful solutions to many scientific applications; however, they usually require long model training time due to large training data sets…”
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