Zobrazeno 1 - 10
of 22 339
pro vyhledávání: '"Sayan, A."'
Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level o
Externí odkaz:
http://arxiv.org/abs/2410.02541
Controlling the dynamics of quantum many-body systems is crucial for developing quantum technologies. This work demonstrates that counter-diabatic (CD) driving provides a powerful tool for steering collective spin systems along entangled trajectories
Externí odkaz:
http://arxiv.org/abs/2409.17198
Autor:
Cochran, Tyler A., Jobst, Bernhard, Rosenberg, Eliott, Lensky, Yuri D., Gyawali, Gaurav, Eassa, Norhan, Will, Melissa, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bourassa, Alexandre, Bovaird, Jenna, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heu, Paula, Higgott, Oscar, Hilton, Jeremy, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Li, Wing Yan, Lill, Alexander T., Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Miao, Kevin C., Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Neill, Charles, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobris, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Gammon-Smith, Adam, Pollmann, Frank, Knap, Michael, Roushan, Pedram
Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emerg
Externí odkaz:
http://arxiv.org/abs/2409.17142
Autor:
Gao, Jun, Krishna, Govind, Yeung, Edith, Yu, Lingxi, Gangopadhyay, Sayan, Chan, Kai-Sum, Huang, Chiao-Tzu, Descamps, Thomas, Reimer, Michael E., Poole, Philip J., Dalacu, Dan, Zwiller, Val, Elshaari, Ali W.
Coherent control of single photon sources is a key requirement for the advancement of photonic quantum technologies. Among them, nanowire-based quantum dot sources are popular due to their potential for on-chip hybrid integration. Here we demonstrate
Externí odkaz:
http://arxiv.org/abs/2409.14964
We investigate the quantum phases of a frustrated antiferromagnetic Heisenberg spin-1/2 model Hamiltonian on a Kagome-strip chain (KSC), a one-dimensional analogue of the Kagome lattice, and construct its phase diagram in an extended exchange paramet
Externí odkaz:
http://arxiv.org/abs/2409.12530
The optical generation of nonequilibrium spin magnetization plays a crucial role in advancing spintronics, providing ultrafast control of magnetization dynamics without the need for magnetic fields. Here, we demonstrate the feasibility of light-induc
Externí odkaz:
http://arxiv.org/abs/2409.12142
Fueled by the influence of real-world networks both in science and society, numerous mathematical models have been developed to understand the structure and evolution of these systems, particularly in a temporal context. Recent advancements in fields
Externí odkaz:
http://arxiv.org/abs/2409.10307
We provide finite-particle convergence rates for the Stein Variational Gradient Descent (SVGD) algorithm in the Kernelized Stein Discrepancy ($\mathsf{KSD}$) and Wasserstein-2 metrics. Our key insight is that the time derivative of the relative entro
Externí odkaz:
http://arxiv.org/abs/2409.08469
In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal contro
Externí odkaz:
http://arxiv.org/abs/2409.07766
Driven by the explosion of data and the impact of real-world networks, a wide array of mathematical models have been proposed to understand the structure and evolution of such systems, especially in the temporal context. Recent advances in areas such
Externí odkaz:
http://arxiv.org/abs/2409.06048