Artificial neural networks (ANNs) have now been widely used for industrial applications and also played more important roles in fundamental research. Although most ANN hardware systems are electronically based, optical implementation is particularly attractive because of its intrinsic parallelism and low energy consumption.

In this thesis, we demonstrate a fully-functioned all optical neural network (AONN), in which the linear operations are programmed by spatial light modulators and Fourier lenses, and the nonlinear optical activation functions are realized with electromagnetically induced transparency in laser-cooled atoms. We demonstrate a scalable AONN with programmable linear operations and tunable nonlinear activation functions. Such an AONN is scalable because all the errors from different optical neurons are independent. Although all-optical deep neural networks (ADNNs) with a few neurons have been experimentally demonstrated with acceptable errors recently, the feasibility of large scale AODNNs remains unknown because error might accumulate inevitably with increasing number of neurons and connections. We verify its scalability by measuring and analyzing errors propagating from a single neuron to the entire network.

Moreover, our hardware system is reconfigurable for different applications without the need of modifying the physical structure. We confirm its capability and feasibility in machine learning by multiple tasks. The AONN successfully classifying the order and disorder phases of a typical statistic Ising model. The demonstrated AONN scheme can be used to construct various ANN architectures with the intrinsic optical parallel
computation. The feasibility of AODNNs is further confirmed by recognizing handwritten digits and fashions with classification rates of 81.8% and 71.3%, respectively.

The other application of AONN is a regression task, quantum state tomography (QST). QST is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the“imaging” technique in the quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. Here, we build an integrated all-optical machine for neural network QST, based on an all-optical neural network (AONN). Experiment results show that AONN can predict the phase parameter of the quantum state accurately. Given that optics is highly desired for quantum interconnections, our AONNQST may contribute to realization of all-optical quantum networks and inspire the ideas combining optical neuromorphic computing with quantum information studies.

Meeting Link: To request for meeting link, please write to phjacma@ust.hk.