摘要
Synthetic dimensions(SDs)opened the door for exploring previously inaccessible phenomena in high-dimensional space.However,construction of synthetic lattices with desired coupling properties is a challenging and unintuitive task.Here,we use deep learning artificial neural networks(ANNs)to construct lattices in real space with a predesigned spectrum of mode eigenvalues,and thus to validly design the dynamics in synthetic mode dimensions.By employing judiciously chosen perturbations(wiggling of waveguides at desired frequencies),we show resonant mode coupling and tailored dynamics in SDs.Two distinct examples are illustrated:one features uniform synthetic mode coupling,and the other showcases the edge defects that allow for tailored light transport and confinement.Furthermore,we demonstrate morphing of light into a topologically protected edge mode with modified Su-Schrieffer-Heeger photonic lattices.Such an ANN-assisted construction of SDs may advance toward“utopian networks,”opening new avenues for fundamental research beyond geometric limitations as well as for applications in mode lasing,optical switching,and communication technologies.
基金
supported by the National Key R&D Program of China(Grant No.2022YFA1404800)
the National Natural Science Foundation of China(Grant Nos.12134006,12274242,11922408,and 12204252)
the China Postdoctoral Science Foundation(Grant Nos.BX2021134 and 2021M701790)
the Natural Science Foundation of Tianjin for Distinguished Young Scholars(Grant No.21JCJQJC00050)
PCSIRT(Grant No.IRT_13R29)
the 111 Project(Grant No.B23045)in China
support from the Croatian-Chinese bilateral project funded by the Ministry of Science and Education in Croatia and the Ministry of Science and Technology in China
support from the project“Implementation of cutting-edge research and its application as part of the Scientific Center of Excellence for Quantum and Complex Systems,and Representations of Lie Algebras,”European Union
European Regional Development Fund
support from the Canada Research Chair program and from NSERC via the Discovery Grant program
