Zobrazeno 1 - 10
of 7 429
pro vyhledávání: '"Hu,Hong"'
Autor:
Van Kirk, Katherine, Kokail, Christian, Kunjummen, Jonathan, Hu, Hong-Ye, Teng, Yanting, Cain, Madelyn, Taylor, Jacob, Yelin, Susanne F., Pichler, Hannes, Lukin, Mikhail
Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algorithm for efficiently learning a large set of non-commuting observables,
Externí odkaz:
http://arxiv.org/abs/2412.18973
Autor:
Mark, Daniel K., Hu, Hong-Ye, Kwan, Joyce, Kokail, Christian, Choi, Soonwon, Yelin, Susanne F.
Understanding the mechanism of high-temperature superconductivity is among the most important problems in physics, for which quantum simulation can provide new insights. However, it remains challenging to characterize superconductivity in existing co
Externí odkaz:
http://arxiv.org/abs/2412.13186
The quality of training data significantly impacts the performance of large language models (LLMs). There are increasing studies using LLMs to rate and select data based on several human-crafted metrics (rules). However, these conventional rule-based
Externí odkaz:
http://arxiv.org/abs/2410.04715
Autor:
Shen, Yizhi, Buzali, Alex, Hu, Hong-Ye, Klymko, Katherine, Camps, Daan, Yelin, Susanne F., Van Beeumen, Roel
Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground state calculation
Externí odkaz:
http://arxiv.org/abs/2409.13691
Quantum machine learning QML algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, c
Externí odkaz:
http://arxiv.org/abs/2408.14697
Autor:
Kornjača, Milan, Hu, Hong-Ye, Zhao, Chen, Wurtz, Jonathan, Weinberg, Phillip, Hamdan, Majd, Zhdanov, Andrii, Cantu, Sergio H., Zhou, Hengyun, Bravo, Rodrigo Araiza, Bagnall, Kevin, Basham, James I., Campo, Joseph, Choukri, Adam, DeAngelo, Robert, Frederick, Paige, Haines, David, Hammett, Julian, Hsu, Ning, Hu, Ming-Guang, Huber, Florian, Jepsen, Paul Niklas, Jia, Ningyuan, Karolyshyn, Thomas, Kwon, Minho, Long, John, Lopatin, Jonathan, Lukin, Alexander, Macrì, Tommaso, Marković, Ognjen, Martínez-Martínez, Luis A., Meng, Xianmei, Ostroumov, Evgeny, Paquette, David, Robinson, John, Rodriguez, Pedro Sales, Singh, Anshuman, Sinha, Nandan, Thoreen, Henry, Wan, Noel, Waxman-Lenz, Daniel, Wong, Tak, Wu, Kai-Hsin, Lopes, Pedro L. S., Boger, Yuval, Gemelke, Nathan, Kitagawa, Takuya, Keesling, Alexander, Gao, Xun, Bylinskii, Alexei, Yelin, Susanne F., Liu, Fangli, Wang, Sheng-Tao
Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant res
Externí odkaz:
http://arxiv.org/abs/2407.02553
Autor:
Zhou, Hengyun, Zhao, Chen, Cain, Madelyn, Bluvstein, Dolev, Duckering, Casey, Hu, Hong-Ye, Wang, Sheng-Tao, Kubica, Aleksander, Lukin, Mikhail D.
Fast, reliable logical operations are essential for the realization of useful quantum computers, as they are required to implement practical quantum algorithms at large scale. By redundantly encoding logical qubits into many physical qubits and using
Externí odkaz:
http://arxiv.org/abs/2406.17653
Autor:
Alexander, Koen, Bahgat, Andrea, Benyamini, Avishai, Black, Dylan, Bonneau, Damien, Burgos, Stanley, Burridge, Ben, Campbell, Geoff, Catalano, Gabriel, Ceballos, Alex, Chang, Chia-Ming, Chung, CJ, Danesh, Fariba, Dauer, Tom, Davis, Michael, Dudley, Eric, Er-Xuan, Ping, Fargas, Josep, Farsi, Alessandro, Fenrich, Colleen, Frazer, Jonathan, Fukami, Masaya, Ganesan, Yogeeswaran, Gibson, Gary, Gimeno-Segovia, Mercedes, Goeldi, Sebastian, Goley, Patrick, Haislmaier, Ryan, Halimi, Sami, Hansen, Paul, Hardy, Sam, Horng, Jason, House, Matthew, Hu, Hong, Jadidi, Mehdi, Johansson, Henrik, Jones, Thomas, Kamineni, Vimal, Kelez, Nicholas, Koustuban, Ravi, Kovall, George, Krogen, Peter, Kumar, Nikhil, Liang, Yong, LiCausi, Nicholas, Llewellyn, Dan, Lokovic, Kimberly, Lovelady, Michael, Manfrinato, Vitor, Melnichuk, Ann, Souza, Mario, Mendoza, Gabriel, Moores, Brad, Mukherjee, Shaunak, Munns, Joseph, Musalem, Francois-Xavier, Najafi, Faraz, O'Brien, Jeremy L., Ortmann, J. Elliott, Pai, Sunil, Park, Bryan, Peng, Hsuan-Tung, Penthorn, Nicholas, Peterson, Brennan, Poush, Matt, Pryde, Geoff J., Ramprasad, Tarun, Ray, Gareth, Rodriguez, Angelita, Roxworthy, Brian, Rudolph, Terry, Saunders, Dylan J., Shadbolt, Pete, Shah, Deesha, Shin, Hyungki, Smith, Jake, Sohn, Ben, Sohn, Young-Ik, Son, Gyeongho, Sparrow, Chris, Staffaroni, Matteo, Stavrakas, Camille, Sukumaran, Vijay, Tamborini, Davide, Thompson, Mark G., Tran, Khanh, Triplet, Mark, Tung, Maryann, Vert, Alexey, Vidrighin, Mihai D., Vorobeichik, Ilya, Weigel, Peter, Wingert, Mathhew, Wooding, Jamie, Zhou, Xinran
Whilst holding great promise for low noise, ease of operation and networking, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions. Here we introduce a manufacturable pl
Externí odkaz:
http://arxiv.org/abs/2404.17570
Recent advances in machine learning have been achieved by using overparametrized models trained until near interpolation of the training data. It was shown, e.g., through the double descent phenomenon, that the number of parameters is a poor proxy fo
Externí odkaz:
http://arxiv.org/abs/2403.08160
Autor:
Evert, Bram, Izquierdo, Zoe Gonzalez, Sud, James, Hu, Hong-Ye, Grabbe, Shon, Rieffel, Eleanor G., Reagor, Matthew J., Wang, Zhihui
Theoretically understanding and experimentally characterizing and modifying the underlying Hamiltonian of a quantum system is of utmost importance in achieving high-fidelity quantum gates for quantum computing. In this work, we explore the use of dyn
Externí odkaz:
http://arxiv.org/abs/2403.07836