Zobrazeno 1 - 10
of 222
pro vyhledávání: '"Li, Boning"'
As digital twins (DTs) to physical communication systems, network simulators can aid the design and deployment of communication networks. However, time-consuming simulations must be run for every new set of network configurations. Learnable digital t
Externí odkaz:
http://arxiv.org/abs/2408.09040
Effective action abstraction is crucial in tackling challenges associated with large action spaces in Imperfect Information Extensive-Form Games (IIEFGs). However, due to the vast state space and computational complexity in IIEFGs, existing methods o
Externí odkaz:
http://arxiv.org/abs/2403.04344
Autor:
Li, Changhao, Li, Boning, Amer, Omar, Shaydulin, Ruslan, Chakrabarti, Shouvanik, Wang, Guoqing, Xu, Haowei, Tang, Hao, Schoch, Isidor, Kumar, Niraj, Lim, Charles, Li, Ju, Cappellaro, Paola, Pistoia, Marco
Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecti
Externí odkaz:
http://arxiv.org/abs/2310.12893
Publikováno v:
Phys. Rev. A 108, L021502 (2023)
The radiative excitation of the 8.3 eV isomeric state of thorium-229 is an outstanding challenge due to the lack of tunable far-ultraviolet (F-UV) sources. In this work, we propose an efficient two-photon pumping scheme for thorium-229 using the opto
Externí odkaz:
http://arxiv.org/abs/2308.16291
Autor:
Tang, Hao, Li, Boning, Song, Yixuan, Liu, Mengren, Xu, Haowei, Wang, Guoqing, Chung, Heejung, Li, Ju
Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental
Externí odkaz:
http://arxiv.org/abs/2307.05394
We propose the deep demixing (DDmix) model, a graph autoencoder that can reconstruct epidemics evolving over networks from partial or aggregated temporal information. Assuming knowledge of the network topology but not of the epidemic model, our goal
Externí odkaz:
http://arxiv.org/abs/2306.07938
Autor:
Li, Boning, Efimov, Timofey, Kumar, Abhishek, Cortes, Jose, Verma, Gunjan, Swami, Ananthram, Segarra, Santiago
Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration. In this paper, we propose a learning-based NDT
Externí odkaz:
http://arxiv.org/abs/2306.06574
We propose a flexible framework for defining the 1-Laplacian of a hypergraph that incorporates edge-dependent vertex weights. These weights are able to reflect varying importance of vertices within a hyperedge, thus conferring the hypergraph model hi
Externí odkaz:
http://arxiv.org/abs/2305.00462
We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks. The proposed method is useful in challenging scenarios where the wireless channel is changing during the FL tr
Externí odkaz:
http://arxiv.org/abs/2304.09329
Autor:
Wang, Guoqing, Madonini, Francesca, Li, Boning, Li, Changhao, Xiang, Jinggang, Villa, Federica, Cappellaro, Paola
Publikováno v:
Adv Quantum Technol. 2023, 2300046
Achieving fast, sensitive, and parallel measurement of a large number of quantum particles is an essential task in building large-scale quantum platforms for different quantum information processing applications such as sensing, computation, simulati
Externí odkaz:
http://arxiv.org/abs/2302.12743