Zobrazeno 1 - 10
of 7 116
pro vyhledávání: '"Hu, Hong"'
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
Autor:
Hu, Hong-Ye, Gu, Andi, Majumder, Swarnadeep, Ren, Hang, Zhang, Yipei, Wang, Derek S., You, Yi-Zhuang, Minev, Zlatko, Yelin, Susanne F., Seif, Alireza
Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few measurements.
Externí odkaz:
http://arxiv.org/abs/2402.17911
Autor:
Hu, Pei-Jin, Chen, Qi-Ling, Chen, Tian-Lu, Kang, Ming-Ming, Guo, Yi-Qing, Luo-Bu, Dan-Zeng, Feng, You-Liang, Gao, Qi, Gou, Quan-Bu, Hu, Hong-Bo, Li, Hai-Jin, Liu, Cheng, Liu, Mao-Yuan, Liu, Wei, Qian, Xiang-Li, Qiao, Bing-Qiang, Su, Jing-Jing, Sun, Hui-Ying, Wang, Xu, Wang, Zhen, Xin, Guang-Guang, Yang, Chao-Wen, Yao, Yu-Hua, Yuan, Qiang, Zhang, Yi
The detection of GW170817/GRB170817A implied the strong association between short gamma-ray bursts (SGRBs) and binary neutron star (BNS) mergers which produce gravitational waves (GWs). More evidence is needed to confirm the association and reveal th
Externí odkaz:
http://arxiv.org/abs/2401.11399
Autor:
Lu, Jonathan Z., Jiao, Lucy, Wolinski, Kristina, Kornjača, Milan, Hu, Hong-Ye, Cantu, Sergio, Liu, Fangli, Yelin, Susanne F., Wang, Sheng-Tao
We propose hybrid digital-analog learning algorithms on Rydberg atom arrays, combining the potentially practical utility and near-term realizability of quantum learning with the rapidly scaling architectures of neutral atoms. Our construction require
Externí odkaz:
http://arxiv.org/abs/2401.02940