分类： 光学 >> 量子光学 提交时间： 2023-02-19
Yuzhi Shi
Qinghua Song
Ivan Toftul
Tongtong Zhu
Yefeng Yu
Weiming Zhu
Din Ping Tsai
Yuri Kivshar
Ai Qun Liu
摘要： Optical tweezers employing forces produced by light underpin important manipulation tools in many areas of applied and biological physics. Conventional optical tweezers are based on refractive optics, and they require excessive auxiliary optical elements to reshape both amplitude and phase, as well as wavevector and angular momentum of light, and thus impose limitations to the overall cost and integration of optical systems. Metamaterials provide both electric and optically induced magnetic response in subwavelength optical structures, and they are highly beneficial to achieve unprecedented control of light required for many applications, also opening new opportunities for optical manipulation. Here, we review the recent advances in the field of optical tweezers employing the physics and concepts of metamaterials (the so-called meta-tweezers) and demonstrate that metamaterial structures could not only advance classical operations with particles, such as trapping, transporting, and sorting, but they uncover exotic optical forces such as pulling and lateral forces. Remarkably, apart from manipulation of particles, metastructures can be powered dynamically by light to realize ingenious meta-robots. We provide an outlook for future opportunities in this area ranging from enhanced particle manipulation to meta-robot actuation.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
Hui Zhang
Jonathan Wei Zhong Lau
Lingxiao Wan
Liang Shi
Hong Cai
Xianshu Luo
Patrick Lo
Chee-Kong Lee
Leong-Chuan Kwek
Ai Qun Liu
摘要： Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever-increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural networks with faster data processing and lower energy usage compared to digital computers. Photonics technology is naturally capable of implementing complex-valued neural networks at no additional hardware cost. Here, we demonstrate the capability of photonic neural networks for predicting the quantum mechanical properties of molecules. To the best of our knowledge, this work is the first to harness photonic technology for machine learning applications in computational chemistry and molecular sciences, such as drug discovery and materials design. We further show that multiple properties can be learned simultaneously in a photonic chip via a multi-task regression learning algorithm, which is also the first of its kind as well, as most previous works focus on implementing a network in the classification task.
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