Zobrazeno 1 - 10
of 21 877
pro vyhledávání: '"Mohseni, A."'
Autor:
Agliardi, Gabriele, Cortiana, Giorgio, Dekusar, Anton, Ghosh, Kumar, Mohseni, Naeimeh, O'Meara, Corey, Valls, Víctor, Yogaraj, Kavitha, Zhuk, Sergiy
Fidelity quantum kernels have shown promise in classification tasks, particularly when a group structure in the data can be identified and exploited through a covariant feature map. In fact, there exist classification problems on which covariant kern
Externí odkaz:
http://arxiv.org/abs/2412.07915
Autor:
Dziubyna, Anna Maria, Śmierzchalski, Tomasz, Gardas, Bartłomiej, Rams, Marek M., Mohseni, Masoud
Optimization problems pose challenges across various fields. In recent years, quantum annealers have emerged as a promising platform for tackling such challenges. To provide a new perspective, we develop a heuristic tensor-network-based algorithm to
Externí odkaz:
http://arxiv.org/abs/2411.16431
Autor:
Mohseni, Masoud, Scherer, Artur, Johnson, K. Grace, Wertheim, Oded, Otten, Matthew, Aadit, Navid Anjum, Bresniker, Kirk M., Camsari, Kerem Y., Chapman, Barbara, Chatterjee, Soumitra, Dagnew, Gebremedhin A., Esposito, Aniello, Fahim, Farah, Fiorentino, Marco, Khalid, Abdullah, Kong, Xiangzhou, Kulchytskyy, Bohdan, Li, Ruoyu, Lott, P. Aaron, Markov, Igor L., McDermott, Robert F., Pedretti, Giacomo, Gajjar, Archit, Silva, Allyson, Sorebo, John, Spentzouris, Panagiotis, Steiner, Ziv, Torosov, Boyan, Venturelli, Davide, Visser, Robert J., Webb, Zak, Zhan, Xin, Cohen, Yonatan, Ronagh, Pooya, Ho, Alan, Beausoleil, Raymond G., Martinis, John M.
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubi
Externí odkaz:
http://arxiv.org/abs/2411.10406
Autor:
McKnight, Shaun, Tunukovic, Vedran, Hifi, Amine, Pierce, Gareth, Mohseni, Ehsan, MacLeod, Charles, OHare, Tom
This study introduces a novel self-supervised learning approach for volumetric segmentation of defect indications captured by phased array ultrasonic testing data from Carbon Fiber Reinforced Polymers (CFRPs). By employing this self-supervised method
Externí odkaz:
http://arxiv.org/abs/2411.07835
Portfolio diversification, traditionally measured through asset correlations and volatilitybased metrics, is fundamental to managing financial risk. However, existing diversification metrics often overlook non-numerical relationships between assets t
Externí odkaz:
http://arxiv.org/abs/2411.06080
Autor:
Mohseni, Mahan, Cunha, Iann, Miravet, Daniel, Rodrigues, Alina Wania, Allami, Hassan, Assi, Ibsal, Korkusinski, Marek, Hawrylak, Pawel
This work presents steps toward the design of Majorana zero modes (MZM) in InAsP quantum dots (QD) embedded in an InP semiconducting nanowire in contact with a p-type superconductor described by the Kitaev Hamiltonian. The single particle spectrum is
Externí odkaz:
http://arxiv.org/abs/2410.22431
Autor:
Mohseni-Sehdeh, Saeed, Saad, Walid
Causal models seek to unravel the cause-effect relationships among variables from observed data, as opposed to mere mappings among them, as traditional regression models do. This paper introduces a novel causal discovery algorithm designed for settin
Externí odkaz:
http://arxiv.org/abs/2410.01221
Autor:
Zhou, Jie, Vincent, Daniel, Acharya, Sudip, Ojo, Solomon, Abrand, Alireza, Liu, Yang, Gong, Jiarui, Liu, Dong, Haessly, Samuel, Shen, Jianping, Xu, Shining, Li, Yiran, Lu, Yi, Stanchu, Hryhorii, Mawst, Luke, Claflin, Bruce, Mohseni, Parsian K., Ma, Zhenqiang, Yu, Shui-Qing
Group IV GeSn double-heterostructure (DHS) lasers offer unique advantages of a direct bandgap and CMOS compatibility. However, further improvements in laser performance have been bottlenecked by limited junction properties of GeSn through conventiona
Externí odkaz:
http://arxiv.org/abs/2409.09752
Autor:
Zhang, Xiangyi, Böhm, Fabian, Valiante, Elisabetta, Noori, Moslem, Van Vaerenbergh, Thomas, Yang, Chan-Woo, Pedretti, Giacomo, Mohseni, Masoud, Beausoleil, Raymond, Rozada, Ignacio
In-memory computing (IMC) has been shown to be a promising approach for solving binary optimization problems while significantly reducing energy and latency. Building on the advantages of parallel computation, we propose an IMC-compatible parallelism
Externí odkaz:
http://arxiv.org/abs/2409.09152
The vast majority of 21st century AI workloads are based on gradient-based deterministic algorithms such as backpropagation. One of the key reasons for the dominance of deterministic ML algorithms is the emergence of powerful hardware accelerators (G
Externí odkaz:
http://arxiv.org/abs/2409.11422