Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Neira, David E Bernal"'
Autor:
Dutta, Siddhant, Karanth, Pavana P, Xavier, Pedro Maciel, de Freitas, Iago Leal, Innan, Nouhaila, Yahia, Sadok Ben, Shafique, Muhammad, Neira, David E. Bernal
The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federated Learning was first proposed as a privacy-preserving alternative to c
Externí odkaz:
http://arxiv.org/abs/2409.11430
Dynamic optimization problems involving discrete decisions have several applications, yet lead to challenging optimization problems that must be addressed efficiently. Combining discrete variables with potentially nonlinear constraints stemming from
Externí odkaz:
http://arxiv.org/abs/2409.09237
Autor:
Chatterjee, Avimita, Rappaport, Sonny, Giri, Anish, Johri, Sonika, Proctor, Timothy, Neira, David E. Bernal, Sathe, Pratik, Lubinski, Thomas
Quantum Hamiltonian simulation is one of the most promising applications of quantum computing and forms the basis for many quantum algorithms. Benchmarking them is an important gauge of progress in quantum computing technology. We present a methodolo
Externí odkaz:
http://arxiv.org/abs/2409.06919
Autor:
Maciejewski, Filip B., Bach, Bao Gia, Dupont, Maxime, Lott, P. Aaron, Sundar, Bhuvanesh, Neira, David E. Bernal, Safro, Ilya, Venturelli, Davide
Quantum approximate optimization is one of the promising candidates for useful quantum computation, particularly in the context of finding approximate solutions to Quadratic Unconstrained Binary Optimization (QUBO) problems. However, the existing qua
Externí odkaz:
http://arxiv.org/abs/2408.07793
Autor:
Peng, Zedong, Cao, Kaiyu, Furman, Kevin C., Li, Can, Grossmann, Ignacio E., Neira, David E. Bernal
The advancement of domain reduction techniques has significantly enhanced the performance of solvers in mathematical programming. This paper delves into the impact of integrating convexification and domain reduction techniques within the Outer- Appro
Externí odkaz:
http://arxiv.org/abs/2407.20973
Autor:
Sorourifar, Farshud, Rouabah, Mohamed Taha, Belaloui, Nacer Eddine, Louamri, Mohamed Messaoud, Chamaki, Diana, Gustafson, Erik J., Tubman, Norm M., Paulson, Joel A., Neira, David E. Bernal
Variational Quantum Eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classical computer to optimize the parameters of a trial wave function, while
Externí odkaz:
http://arxiv.org/abs/2407.07963
Autor:
Rieffel, Eleanor G., Asanjan, Ata Akbari, Alam, M. Sohaib, Anand, Namit, Neira, David E. Bernal, Block, Sophie, Brady, Lucas T., Cotton, Steve, Izquierdo, Zoe Gonzalez, Grabbe, Shon, Gustafson, Erik, Hadfield, Stuart, Lott, P. Aaron, Maciejewski, Filip B., Mandrà, Salvatore, Marshall, Jeffrey, Mossi, Gianni, Bauza, Humberto Munoz, Saied, Jason, Suri, Nishchay, Venturelli, Davide, Wang, Zhihui, Biswas, Rupak
Publikováno v:
Future Generation Computer Systems (2024)
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiment
Externí odkaz:
http://arxiv.org/abs/2406.15601
Autor:
Sorourifar, Farshud, Chamaki, Diana, Tubman, Norm M., Paulson, Joel A., Neira, David E. Bernal
Quantum computers currently rely on a hybrid quantum-classical approach known as Variational Quantum Algorithms (VQAs) to solve problems. Still, there are several challenges with VQAs on the classical computing side: it corresponds to a black-box opt
Externí odkaz:
http://arxiv.org/abs/2406.14627
Autor:
Wang, Jialu, Peng, Zedong, Hughes, Ryan, Bhattacharyya, Debangsu, Neira, David E. Bernal, Dowling, Alexander W.
Model-based design of experiments (MBDoE) is a powerful framework for selecting and calibrating science-based mathematical models from data. This work extends popular MBDoE workflows by proposing a convex mixed integer (non)linear programming (MINLP)
Externí odkaz:
http://arxiv.org/abs/2406.09557
Healthcare industries frequently handle sensitive and proprietary data, and due to strict privacy regulations, they are often reluctant to share data directly. In today's context, Federated Learning (FL) stands out as a crucial remedy, facilitating t
Externí odkaz:
http://arxiv.org/abs/2405.07735