Zobrazeno 1 - 10
of 2 526
pro vyhledávání: '"Kong, Jian"'
Autor:
Sahin, Haydar, Akgün, Hakan, Siu, Zhuo Bin, Rafi-Ul-Islam, S. M., Kong, Jian Feng, Jalil, Mansoor B. A., Lee, Ching Hua
The erratic nature of chaotic behavior is thought to erode the stability of periodic behavior, including topological oscillations. However, we discover that in the presence of chaos, non-trivial topology not only endures but also provides robust prot
Externí odkaz:
http://arxiv.org/abs/2411.07522
Autor:
Leong, Fong Yew, Koh, Dax Enshan, Kong, Jian Feng, Goh, Siong Thye, Khoo, Jun Yong, Ewe, Wei-Bin, Li, Hongying, Thompson, Jayne, Poletti, Dario
Publikováno v:
AVS Quantum Sci. 6, 033802 (2024)
We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable cost function incorporating a linear combination of overlap history st
Externí odkaz:
http://arxiv.org/abs/2406.08755
The development of single-platform qubits, predominant for most of the last few decades, has driven the progress of quantum information technologies but also highlighted the limitations of various platforms. Some inherent issues such as charge/spin n
Externí odkaz:
http://arxiv.org/abs/2404.05174
Autor:
You, Jia-Bin, Kong, Jian Feng, Aghamalyan, Davit, Mok, Wai-Keong, Lim, Kian Hwee, Ye, Jun, Png, Ching Eng, García-Vidal, Francisco J.
We explore a scheme for entanglement generation and optimization in giant atoms by coupling them to finite one-dimensional arrays of spins that behave as cavities. We find that high values for the concurrence can be achieved in small-sized cavities,
Externí odkaz:
http://arxiv.org/abs/2403.00264
We propose a novel rapid, high-fidelity, and noise-resistant scheme to generate many-body entanglement between multiple qubits stabilized by dissipation into a 1D bath. Using a carefully designed time-dependent drive, our scheme achieves a provably e
Externí odkaz:
http://arxiv.org/abs/2309.12705
We explore the interplay between symmetry and randomness in quantum information. Adopting a geometric approach, we consider states as $H$-equivalent if related by a symmetry transformation characterized by the group $H$. We then introduce the Haar me
Externí odkaz:
http://arxiv.org/abs/2309.05253
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 33 No. 11, pp. 3669-3690 (2023)
We assess the use of variational quantum imaginary time evolution for solving partial differential equations. Our results demonstrate that real-amplitude ansaetze with full circular entangling layers lead to higher-fidelity solutions compared to thos
Externí odkaz:
http://arxiv.org/abs/2307.07173
Autor:
Kong, Jian Feng, Ren, Yuhua, Tey, M. S. Nicholas, Ho, Pin, Khoo, Khoong Hong, Chen, Xiaoye, Soumyanarayanan, Anjan
The interplay of magnetic interactions in chiral multilayer films gives rise to nanoscale topological spin textures, which form attractive elements for next-generation computing. Quantifying these interactions requires several specialized, time-consu
Externí odkaz:
http://arxiv.org/abs/2305.02954
Impedance responses and size-dependent resonances in topolectrical circuits via the method of images
Autor:
Sahin, Haydar, Siu, Zhuo Bin, Rafi-Ul-Islam, S. M., Kong, Jian Feng, Jalil, Mansoor B. A., Lee, Ching Hua
Publikováno v:
PhysRevB.107.245114 (2023)
Resonances in an electric circuit occur when capacitive and inductive components are present together. Such resonances appear in admittance measurements depending on the circuit's parameters and the driving AC frequency. In this study, we analyze the
Externí odkaz:
http://arxiv.org/abs/2212.06165
Autor:
Kong, Jian-Gang, Zhao, Ke-Lin, Li, Jian, Li, Qing-Xu, Liu, Yu, Zhang, Rui, Zhu, Jia-Ji, Chang, Kai
Supervised machine learning algorithms, such as graph neural networks (GNN), have successfully predicted material properties. However, the superior performance of GNN usually relies on end-to-end learning on large material datasets, which may lose th
Externí odkaz:
http://arxiv.org/abs/2211.03563