Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Shehab Omar"'
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 3, Pp 1-11 (2022)
Research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely classical counterparts. Although theoretical work has shown
Externí odkaz:
https://doaj.org/article/a3bf511f1b6c4747a0b216f7adde14c4
The influence of noise on quantum dynamics is one of the main factors preventing current quantum processors from performing accurate quantum computations. Sufficient noise characterization and modeling can provide key insights into the effect of nois
Externí odkaz:
http://arxiv.org/abs/2412.16092
Autor:
Doga, Hakan, Raubenolt, Bryan, Cumbo, Fabio, Joshi, Jayadev, DiFilippo, Frank P., Qin, Jun, Blankenberg, Daniel, Shehab, Omar
Despite the recent advancements by deep learning methods such as AlphaFold2, \textit{in silico} protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whe
Externí odkaz:
http://arxiv.org/abs/2312.00875
Autor:
Basu, Saugata, Born, Jannis, Bose, Aritra, Capponi, Sara, Chalkia, Dimitra, Chan, Timothy A, Doga, Hakan, Flother, Frederik F., Getz, Gad, Goldsmith, Mark, Gujarati, Tanvi, Guzman-Saenz, Aldo, Iliopoulos, Dimitrios, Jones, Gavin O., Knecht, Stefan, Madan, Dhiraj, Maniscalco, Sabrina, Mariella, Nicola, Morrone, Joseph A., Najafi, Khadijeh, Pati, Pushpak, Platt, Daniel, Rapsomaniki, Maria Anna, Ray, Anupama, Rhrissorrakrai, Kahn, Shehab, Omar, Tavernelli, Ivano, Tolunay, Meltem, Utro, Filippo, Woerner, Stefan, Zhuk, Sergiy, Garcia, Jeannette M., Parida, Laxmi
In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to the expectation that in the near future quantum computers will be capable of performing simulations for natural s
Externí odkaz:
http://arxiv.org/abs/2307.05734
Autor:
Ghosh, Kumar J. B., Yogaraj, Kavitha, Agliardi, Gabriele, Sabino, Piergiacomo, Fernández-Campoamor, Marina, Bernabé-Moreno, Juan, Cortiana, Giorgio, Shehab, Omar, O'Meara, Corey
We generalize the Approximate Quantum Compiling algorithm into a new method for CNOT-depth reduction, which is apt to process wide target quantum circuits. Combining this method with state-of-the-art techniques for error mitigation and circuit compil
Externí odkaz:
http://arxiv.org/abs/2305.09501
Autor:
Agliardi, Gabriele, O'Meara, Corey, Yogaraj, Kavitha, Ghosh, Kumar, Sabino, Piergiacomo, Fernández-Campoamor, Marina, Cortiana, Giorgio, Bernabé-Moreno, Juan, Tacchino, Francesco, Mezzacapo, Antonio, Shehab, Omar
Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis. For instance in the domain of energy economics, accurate and timely risk management demands for efficient simulation of millions of scenario
Externí odkaz:
http://arxiv.org/abs/2304.10385
Publikováno v:
2022
In recent years, research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely classical counterparts. Although theoretic
Externí odkaz:
http://arxiv.org/abs/2112.06211
Variational quantum algorithms are poised to have significant impact on high-dimensional optimization, with applications in classical combinatorics, quantum chemistry, and condensed matter. Nevertheless, the optimization landscape of these algorithms
Externí odkaz:
http://arxiv.org/abs/2112.02190
Autor:
Nash, Dustin, Shah, Maully J., Shehab, Omar, Jones, Andrea L., Iyer, Ramesh, Vetter, Victoria, Janson, Christopher
Publikováno v:
In Heart Rhythm May 2024 21(5):581-589
Autor:
Shehab, Omar, Kim, Isaac H., Nguyen, Nhung H., Landsman, Kevin, Alderete, Cinthia H., Zhu, Daiwei, Monroe, C., Linke, Norbert M.
We introduce an approach to improve the accuracy and reduce the sample complexity of near term quantum-classical algorithms. We construct a simpler initial parameterized quantum state, or ansatz, based on the past causal cone of each observable, gene
Externí odkaz:
http://arxiv.org/abs/1906.00476