Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Pistoia, Marco"'
The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for combinatorial optimization that has been demonstrated to scale better than state-of-the-art classical solvers for some problems. For a given problem instance, QAOA perfo
Externí odkaz:
http://arxiv.org/abs/2408.00557
Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data to maintain model accuracy. This is particularly important in applicat
Externí odkaz:
http://arxiv.org/abs/2406.12008
Autor:
DeCross, Matthew, Haghshenas, Reza, Liu, Minzhao, Rinaldi, Enrico, Gray, Johnnie, Alexeev, Yuri, Baldwin, Charles H., Bartolotta, John P., Bohn, Matthew, Chertkov, Eli, Cline, Julia, Colina, Jonhas, DelVento, Davide, Dreiling, Joan M., Foltz, Cameron, Gaebler, John P., Gatterman, Thomas M., Gilbreth, Christopher N., Giles, Joshua, Gresh, Dan, Hall, Alex, Hankin, Aaron, Hansen, Azure, Hewitt, Nathan, Hoffman, Ian, Holliman, Craig, Hutson, Ross B., Jacobs, Trent, Johansen, Jacob, Lee, Patricia J., Lehman, Elliot, Lucchetti, Dominic, Lykov, Danylo, Madjarov, Ivaylo S., Mathewson, Brian, Mayer, Karl, Mills, Michael, Niroula, Pradeep, Pino, Juan M., Roman, Conrad, Schecter, Michael, Siegfried, Peter E., Tiemann, Bruce G., Volin, Curtis, Walker, James, Shaydulin, Ruslan, Pistoia, Marco, Moses, Steven. A., Hayes, David, Neyenhuis, Brian, Stutz, Russell P., Foss-Feig, Michael
Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized
Externí odkaz:
http://arxiv.org/abs/2406.02501
Autor:
Chen, Chun-Fu, Moriarty, Bill, Hu, Shaohan, Moran, Sean, Pistoia, Marco, Piuri, Vincenzo, Samarati, Pierangela
The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerprints, voices, retina/facial scans, or gait/motion/gestures data, enabl
Externí odkaz:
http://arxiv.org/abs/2405.15062
The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies. The massive collection and usage of data, however, pose an increasing risk for people's private and sen
Externí odkaz:
http://arxiv.org/abs/2405.14981
Variational quantum algorithms (VQAs) are a broad class of algorithms with many applications in science and industry. Applying a VQA to a problem involves optimizing a parameterized quantum circuit by maximizing or minimizing a cost function. A parti
Externí odkaz:
http://arxiv.org/abs/2405.10941
Autor:
Heredge, Jamie, Kumar, Niraj, Herman, Dylan, Chakrabarti, Shouvanik, Yalovetzky, Romina, Sureshbabu, Shree Hari, Li, Changhao, Pistoia, Marco
Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collaborative learning. Motivated by the increasing success of recovering in
Externí odkaz:
http://arxiv.org/abs/2405.08801
We demonstrated for the first time quantum-safe high-speed 100 Gbps site-to-site IPsec tunnels secured using Quantum Key Distribution (QKD) technology. The demonstration was conducted between two JPMorgan Chase Data Centers (DCs) in an air-gapped env
Externí odkaz:
http://arxiv.org/abs/2405.04415
Autor:
Perlin, Michael A., Shaydulin, Ruslan, Hall, Benjamin P., Minssen, Pierre, Li, Changhao, Dubey, Kabir, Rines, Rich, Anschuetz, Eric R., Pistoia, Marco, Gokhale, Pranav
Combinatorial optimization problems that arise in science and industry typically have constraints. Yet the presence of constraints makes them challenging to tackle using both classical and quantum optimization algorithms. We propose a new quantum alg
Externí odkaz:
http://arxiv.org/abs/2403.05653
Suppression of diabatic transitions in quantum adiabatic evolution stands as a significant challenge for ground state preparations. Counterdiabatic driving has been proposed to compensate for diabatic losses and achieve shortcut to adiabaticity. Howe
Externí odkaz:
http://arxiv.org/abs/2403.01854