A hybrid-frequency on-chip programmable synthetic-dimension simulator with arbitrary couplings
Xiao-Dong Zeng, Zhao-An Wang, Jia-Ming Ren, Yi-Tao Wang, Chun Ao, Wei Liu, Nai-Jie Guo, Lin-Ke Xie, Jun-You Liu, Yu-Hang Ma, Ya-Qi Wu, Shuang Wang, Pei-Yun Li, Zong-Quan Zhou, Mu Yang, Jin-Shi Xu, Xi-Wang Luo, Jian-Shun Tang, Chuan-Feng Li, Guang-Can Guo

TL;DR
This paper introduces a hybrid-frequency on-chip synthetic-dimension simulator that enables arbitrary coupling configurations, including long-range and asymmetric couplings, demonstrated on a lithium niobate photonic chip for complex physical system simulations.
Contribution
It presents a novel hybrid-frequency architecture combining intra- and inter-resonant sites, allowing scalable, reconfigurable synthetic dimensions with diverse coupling schemes on a single chip.
Findings
Successfully simulated various lattice models, including Hall, Creutz, and SSH models.
Achieved direct bandstructure readout of the SSH model, distinguishing it from previous works.
Observed phenomena like spin-momentum locking and topological flat bands with reduced experimental complexity.
Abstract
High-performance photonic chips provide a powerful platform for analog computing, enabling the simulation of high-dimensional physical systems using low-dimensional devices with additional synthetic dimensions. The realization of large-scale complex simulations necessitates an architecture capable of arbitrary coupling configurations (encompassing symmetric, asymmetric and long-range coupling schemes) which is also crucial for scaling up. Previous approaches rely on excessive physical components to introduce asymmetric coupling, however, are restricted in reconfiguring and scaling by the relatively complicated structures. Here, to solve this problem, we propose a hybrid-frequency synthetic-dimension simulator architecture that combines both intra-resonant and inter-resonant frequency-lattice sites, and experimentally demonstrate it using the thin-film lithium niobate (TFLN) photonic…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Photonic and Optical Devices · Photorefractive and Nonlinear Optics
