Zobrazeno 1 - 10
of 564
pro vyhledávání: '"Ding, Yunhong"'
Autor:
Ekici, Cagin, Yu, Yonghe, Adcock, Jeremy C., Muthali, Alif Laila, Zahidy, Mujtaba, Tan, Heyun, Lin, Zhongjin, Li, Hao, Oxenløwe, Leif K., Cai, Xinlun, Ding, Yunhong
Heralded photons from a silicon source are temporally multiplexed utilizing thin film lithium niobate photonics. The time-multiplexed source, operating at a rate of R = 62.2 MHz, enhances single photon probability by 3.25 $\pm$ 0.05.
Externí odkaz:
http://arxiv.org/abs/2312.05280
Autor:
Hansen, Mikkel T., Ulsig, Emil Z., Labbe, Fabien, Madsen, Magnus L., Ding, Yunhong, Rottwitt, Karsten, Volet, Nicolas
Publikováno v:
Front. Photon., 06 December 2023 Sec. Nonlinear Optics
A double-ridge waveguide is designed for efficient and robust second-harmonic generation (SHG) using the thin-film lithium-niobate-on-insulator (LNOI) platform. Perfect phase matching (PhM) is achieved between the fundamental waveguide mode at 1550 n
Externí odkaz:
http://arxiv.org/abs/2311.14399
We present and experimentally evaluate using transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers. Our approach involves pre-training th
Externí odkaz:
http://arxiv.org/abs/2308.11630
Autor:
Ekici, Cagin, Yu, Yonghe, Adcock, Jeremy C., Muthali, Alif Laila, Tan, Heyun, Li, Hao, Oxenløwe, Leif Katsuo, Cai, Xinlun, Ding, Yunhong
We experimentally demonstrate a room-temperature, voltage controlled, short-term quantum photonics memory on a lithium niobate chip. Our chip is capable of resolving 100 ps time steps with 0.74 dB loss per round-trip.
Externí odkaz:
http://arxiv.org/abs/2301.04140
Autor:
Wei, Yanxian, Zhou, Hailong, Ding, Yunhong, Cheng, Zihao, Huang, Dongmei, Wai, P. K. A., Dong, Jianji, Zhang, Xinliang
The integration density of photonic integrated circuits has been limited by light coupling between waveguides. Traditional approaches to layout the waveguide with high density are based on refractive index engineering to suppress the light coupling b
Externí odkaz:
http://arxiv.org/abs/2211.16811
We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data. Our approach uses <10\% of experimental data needed for best performance and outperforms analytical models for a Mach-Zehnder
Externí odkaz:
http://arxiv.org/abs/2211.16038
Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are especially well-suited for imp
Externí odkaz:
http://arxiv.org/abs/2210.09171
Publikováno v:
Quantum Science and Technology 7.2 (2022): 025025
Efficient generation of single photons is one of the key challenges of building photonic quantum technology, such as quantum computers and long-distance quantum networks. Photon source multiplexing -- where successful pair generation is heralded by t
Externí odkaz:
http://arxiv.org/abs/2207.02647
Autor:
Adcock, Jeremy C., Bao, Jueming, Chi, Yulin, Chen, Xiaojiong, Bacco, Davide, Gong, Qihuang, Oxenløwe, Leif K., Wang, Jianwei, Ding, Yunhong
Publikováno v:
IEEE Journal of Selected Topics in Quantum Electronics 27.2 (2020): 1-24
Quantum technology is poised to enable a step change in human capability for computing, communications and sensing. Photons are indispensable as carriers of quantum information - they travel at the fastest possible speed and readily protected from de
Externí odkaz:
http://arxiv.org/abs/2207.02644
We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes. The neural-network model outperforms physics-based models for a c
Externí odkaz:
http://arxiv.org/abs/2111.14787