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of 20 213
pro vyhledávání: '"Fatemi AS"'
We propose an architecture for bit flip error correction of Andreev spins that is protected by Kramers' degeneracy. Specifically, we show that a coupling network of linear inductors results in a static Hamiltonian composed of the stabilizers of a bit
Externí odkaz:
http://arxiv.org/abs/2412.16116
Bosonic encodings of quantum information offer hardware-efficient, noise-biased approaches to quantum error correction relative to qubit register encodings. Implementations have focused in particular on error correction of stored, idle quantum inform
Externí odkaz:
http://arxiv.org/abs/2412.08870
Retrieval Augmented Generation (RAG) has emerged as a widely adopted approach to mitigate the limitations of large language models (LLMs) in answering domain-specific questions. Previous research has predominantly focused on improving the accuracy an
Externí odkaz:
http://arxiv.org/abs/2411.16133
Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting such testing in a cost-effective and scalable way. Neural rendering me
Externí odkaz:
http://arxiv.org/abs/2411.16816
This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) to enhance information retrieval and query response systems for university-related question answering. By systematical
Externí odkaz:
http://arxiv.org/abs/2411.06237
Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks, including language understanding, reasoning, and generation. However, general-domain LLMs often struggle with financial tas
Externí odkaz:
http://arxiv.org/abs/2411.02476
Autor:
Fatemi, Sorouralsadat, Hu, Yuheng
Financial trading has been a challenging task, as it requires the integration of vast amounts of data from various modalities. Traditional deep learning and reinforcement learning methods require large training data and often involve encoding various
Externí odkaz:
http://arxiv.org/abs/2411.08899
Autor:
Fatemi, Sorouralsadat, Hu, Yuheng
While Large Language Models (LLMs) have shown impressive capabilities in numerous Natural Language Processing (NLP) tasks, they still struggle with financial question answering (QA), particularly when numerical reasoning is required. Recently, LLM-ba
Externí odkaz:
http://arxiv.org/abs/2410.21741
Causality is essential in scientific research, enabling researchers to interpret true relationships between variables. These causal relationships are often represented by causal graphs, which are directed acyclic graphs. With the recent advancements
Externí odkaz:
http://arxiv.org/abs/2410.15939
Autor:
Gyawali, Gaurav, Cochran, Tyler, Lensky, Yuri, Rosenberg, Eliott, Karamlou, Amir H., Kechedzhi, Kostyantyn, Berndtsson, Julia, Westerhout, Tom, Asfaw, Abraham, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., 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., Das, Sayan, Debroy, Dripto M., De Lorenzo, Laura, Barba, Alexander Del Toro, Demura, Sean, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Fatemi, Reza, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hill, Gordon, Hilton, Jeremy, Hoffmann, Markus R., Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Joonho, Lee, Kenny, Lester, Brian J., Li, Wing Yan, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Neill, Charles, Nersisyan, Ani, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rocque, Charles, Rubin, Nicholas C., Saei, Negar, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szalay, Marco, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vdovichev, Sergey, Vidal, Guifré, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., 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, Zobrist, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Kovrizhin, Dmitry, Knolle, Johannes, Halimeh, Jad C., Aleiner, Igor, Moessner, Roderich, Roushan, Pedram
One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient pro
Externí odkaz:
http://arxiv.org/abs/2410.06557