Zobrazeno 1 - 10
of 392
pro vyhledávání: '"Glick, Jennifer"'
Autor:
Alexeev, Yuri, Amsler, Maximilian, Baity, Paul, Barroca, Marco Antonio, Bassini, Sanzio, Battelle, Torey, Camps, Daan, Casanova, David, Choi, Young Jai, Chong, Frederic T., Chung, Charles, Codella, Chris, Corcoles, Antonio D., Cruise, James, Di Meglio, Alberto, Dubois, Jonathan, Duran, Ivan, Eckl, Thomas, Economou, Sophia, Eidenbenz, Stephan, Elmegreen, Bruce, Fare, Clyde, Faro, Ismael, Fernández, Cristina Sanz, Ferreira, Rodrigo Neumann Barros, Fuji, Keisuke, Fuller, Bryce, Gagliardi, Laura, Galli, Giulia, Glick, Jennifer R., Gobbi, Isacco, Gokhale, Pranav, Gonzalez, Salvador de la Puente, Greiner, Johannes, Gropp, Bill, Grossi, Michele, Gull, Emanuel, Healy, Burns, Huang, Benchen, Humble, Travis S., Ito, Nobuyasu, Izmaylov, Artur F., Javadi-Abhari, Ali, Jennewein, Douglas, Jha, Shantenu, Jiang, Liang, Jones, Barbara, de Jong, Wibe Albert, Jurcevic, Petar, Kirby, William, Kister, Stefan, Kitagawa, Masahiro, Klassen, Joel, Klymko, Katherine, Koh, Kwangwon, Kondo, Masaaki, Kurkcuoglu, Doga Murat, Kurowski, Krzysztof, Laino, Teodoro, Landfield, Ryan, Leininger, Matt, Leyton-Ortega, Vicente, Li, Ang, Lin, Meifeng, Liu, Junyu, Lorente, Nicolas, Luckow, Andre, Martiel, Simon, Martin-Fernandez, Francisco, Martonosi, Margaret, Marvinney, Claire, Medina, Arcesio Castaneda, Merten, Dirk, Mezzacapo, Antonio, Michielsen, Kristel, Mitra, Abhishek, Mittal, Tushar, Moon, Kyungsun, Moore, Joel, Motta, Mario, Na, Young-Hye, Nam, Yunseong, Narang, Prineha, Ohnishi, Yu-ya, Ottaviani, Daniele, Otten, Matthew, Pakin, Scott, Pascuzzi, Vincent R., Penault, Ed, Piontek, Tomasz, Pitera, Jed, Rall, Patrick, Ravi, Gokul Subramanian, Robertson, Niall, Rossi, Matteo, Rydlichowski, Piotr, Ryu, Hoon, Samsonidze, Georgy, Sato, Mitsuhisa, Saurabh, Nishant, Sharma, Vidushi, Sharma, Kunal, Shin, Soyoung, Slessman, George, Steiner, Mathias, Sitdikov, Iskandar, Suh, In-Saeng, Switzer, Eric, Tang, Wei, Thompson, Joel, Todo, Synge, Tran, Minh, Trenev, Dimitar, Trott, Christian, Tseng, Huan-Hsin, Tureci, Esin, Valinas, David García, Vallecorsa, Sofia, Wever, Christopher, Wojciechowski, Konrad, Wu, Xiaodi, Yoo, Shinjae, Yoshioka, Nobuyuki, Yu, Victor Wen-zhe, Yunoki, Seiji, Zhuk, Sergiy, Zubarev, Dmitry
Publikováno v:
Future Generation Computer Systems, Volume 160, November 2024, Pages 666-710
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of the
Externí odkaz:
http://arxiv.org/abs/2312.09733
Quantum machine learning (QML) is a fast-growing discipline within quantum computing. One popular QML algorithm, quantum kernel estimation, uses quantum circuits to estimate a similarity measure (kernel) between two classical feature vectors. Given a
Externí odkaz:
http://arxiv.org/abs/2307.04980
Autor:
Alexeev, Yuri, Amsler, Maximilian, Barroca, Marco Antonio, Bassini, Sanzio, Battelle, Torey, Camps, Daan, Casanova, David, Choi, Young Jay, Chong, Frederic T., Chung, Charles, Codella, Christopher, Córcoles, Antonio D., Cruise, James, Di Meglio, Alberto, Duran, Ivan, Eckl, Thomas, Economou, Sophia, Eidenbenz, Stephan, Elmegreen, Bruce, Fare, Clyde, Faro, Ismael, Fernández, Cristina Sanz, Ferreira, Rodrigo Neumann Barros, Fuji, Keisuke, Fuller, Bryce, Gagliardi, Laura, Galli, Giulia, Glick, Jennifer R., Gobbi, Isacco, Gokhale, Pranav, de la Puente Gonzalez, Salvador, Greiner, Johannes, Gropp, Bill, Grossi, Michele, Gull, Emanuel, Healy, Burns, Hermes, Matthew R., Huang, Benchen, Humble, Travis S., Ito, Nobuyasu, Izmaylov, Artur F., Javadi-Abhari, Ali, Jennewein, Douglas, Jha, Shantenu, Jiang, Liang, Jones, Barbara, de Jong, Wibe Albert, Jurcevic, Petar, Kirby, William, Kister, Stefan, Kitagawa, Masahiro, Klassen, Joel, Klymko, Katherine, Koh, Kwangwon, Kondo, Masaaki, Kürkçüog̃lu, Dog̃a Murat, Kurowski, Krzysztof, Laino, Teodoro, Landfield, Ryan, Leininger, Matt, Leyton-Ortega, Vicente, Li, Ang, Lin, Meifeng, Liu, Junyu, Lorente, Nicolas, Luckow, Andre, Martiel, Simon, Martin-Fernandez, Francisco, Martonosi, Margaret, Marvinney, Claire, Medina, Arcesio Castaneda, Merten, Dirk, Mezzacapo, Antonio, Michielsen, Kristel, Mitra, Abhishek, Mittal, Tushar, Moon, Kyungsun, Moore, Joel, Mostame, Sarah, Motta, Mario, Na, Young-Hye, Nam, Yunseong, Narang, Prineha, Ohnishi, Yu-ya, Ottaviani, Daniele, Otten, Matthew, Pakin, Scott, Pascuzzi, Vincent R., Pednault, Edwin, Piontek, Tomasz, Pitera, Jed, Rall, Patrick, Ravi, Gokul Subramanian, Robertson, Niall, Rossi, Matteo A.C., Rydlichowski, Piotr, Ryu, Hoon, Samsonidze, Georgy, Sato, Mitsuhisa, Saurabh, Nishant, Sharma, Vidushi, Sharma, Kunal, Shin, Soyoung, Slessman, George, Steiner, Mathias, Sitdikov, Iskandar, Suh, In-Saeng, Switzer, Eric D., Tang, Wei, Thompson, Joel, Todo, Synge, Tran, Minh C., Trenev, Dimitar, Trott, Christian, Tseng, Huan-Hsin, Tubman, Norm M., Tureci, Esin, Valiñas, David García, Vallecorsa, Sofia, Wever, Christopher, Wojciechowski, Konrad, Wu, Xiaodi, Yoo, Shinjae, Yoshioka, Nobuyuki, Yu, Victor Wen-zhe, Yunoki, Seiji, Zhuk, Sergiy, Zubarev, Dmitry
Publikováno v:
In Future Generation Computer Systems November 2024 160:666-710
Publikováno v:
PRX Quantum 3, 030323, 2022
Variational quantum circuits are used in quantum machine learning and variational quantum simulation tasks. Designing good variational circuits or predicting how well they perform for given learning or optimization tasks is still unclear. Here we dis
Externí odkaz:
http://arxiv.org/abs/2111.04225
Autor:
Fuller, Bryce, Hadfield, Charles, Glick, Jennifer R., Imamichi, Takashi, Itoko, Toshinari, Thompson, Richard J., Jiao, Yang, Kagele, Marna M., Blom-Schieber, Adriana W., Raymond, Rudy, Mezzacapo, Antonio
Combinatorial problems are formulated to find optimal designs within a fixed set of constraints. They are commonly found across diverse engineering and scientific domains. Understanding how to best use quantum computers for combinatorial optimization
Externí odkaz:
http://arxiv.org/abs/2111.03167
Autor:
Glick, Jennifer R., Gujarati, Tanvi P., Corcoles, Antonio D., Kim, Youngseok, Kandala, Abhinav, Gambetta, Jay M., Temme, Kristan
Publikováno v:
Nat. Phys. 20, 479-483 (2024)
The use of kernel functions is a common technique to extract important features from data sets. A quantum computer can be used to estimate kernel entries as transition amplitudes of unitary circuits. Quantum kernels exist that, subject to computation
Externí odkaz:
http://arxiv.org/abs/2105.03406
Autor:
Wu, Sau Lan, Sun, Shaojun, Guan, Wen, Zhou, Chen, Chan, Jay, Cheng, Chi Lung, Pham, Tuan, Qian, Yan, Wang, Alex Zeng, Zhang, Rui, Livny, Miron, Glick, Jennifer, Barkoutsos, Panagiotis Kl., Woerner, Stefan, Tavernelli, Ivano, Carminati, Federico, Di Meglio, Alberto, Li, Andy C. Y., Lykken, Joseph, Spentzouris, Panagiotis, Chen, Samuel Yen-Chi, Yoo, Shinjae, Wei, Tzu-Chieh
Publikováno v:
Phys. Rev. Research 3, 033221 (2021)
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In this study, we employ a support vector machine with a quantum kernel e
Externí odkaz:
http://arxiv.org/abs/2104.05059
Autor:
Zhang, Leanne, Rosen, Joseph G., Cooper, Lyra, Olatunde, Praise F., Pelaez, Danielle, Sherman, Susan G., Park, Ju Nyeong, Glick, Jennifer L.
Publikováno v:
In SSM - Qualitative Research in Health December 2023 4