Optical Neural Network Architecture for Deep Learning with Temporal Synthetic Dimension

The physical concept of synthetic dimensions has recently been introduced into optics.The fundamental physics and applications are not yet fully understood,and this report explores an approach to optical neural networks using synthetic dimension in time domain,by theoretically proposing to utilize a single resonator network,where the arrival times of optical pulses are interconnected to construct a temporal synthetic dimension.The set of pulses in each roundtrip therefore provides the sites in each layer in the optical neural network,and can be linearly transformed with splitters and delay lines,including the phase modulators,when pulses circulate inside the network.Such linear transformation can be arbitrarily controlled by applied modulation phases,which serve as the building block of the neural network together with a nonlinear component for pulses.We validate the functionality of the proposed optical neural network for the deep learning purpose with examples handwritten digit recognition and optical pulse train distribution classification problems.This proof of principle computational work explores the new concept of developing a photonics-based machine learning in a single ring network using synthetic dimensions,which allows flexibility and easiness of reconfiguration with complex functionality in achieving desired optical tasks.

相干激发的氮分子离子中的光子存留现象

在量子光学中,量子相干性是光信息处理和光场调控的核心.碱金属蒸汽,虽然存在很多缺点,但是由于其具有易激发、偶极跃迁强和相干时间长的优点被广泛用于量子光学研究中.本文提出并实验上展示了800 nm飞秒激光相干激发的N_(2)^(+)中的光子存留现象.由于量子相干性的存在,存留在系统中的光子将在飞秒激光离开后的几十皮秒时间内持续再辐射.由于再辐射的光子较弱,难以直接测量.本文提出通过延时注入一束1580 nm的飞秒激光读取存留光子的方案.其核心思想是借助延时注入的激光与存留光子的双光子吸收,进一步激发N_(2)^(+)产生强的329.3 nm辐射.研究者也表明了N_(2)^(+)激发态布居对于信号读取的重要作用.这一新发现揭示了N_(2)^(+)中量子相干调控的物理机制,打开了在远程大气中进行光学信息存储的前景.此外,以强场电离产生的分子离子为量子系统,有利于进一步探索光与物质相互作用的基本过程.