Shedding Light on the Future: Exploring Quantum Neural Networks through Optics

• URL: http://arxiv.org/abs/2409.02533v1
• Date: Wed, 4 Sep 2024 08:49:57 GMT
• Title: Shedding Light on the Future: Exploring Quantum Neural Networks through Optics
• Authors: Shang Yu, Zhian Jia, Aonan Zhang, Ewan Mer, Zhenghao Li, Valerio Crescimanna, Kuan-Cheng Chen, Raj B. Patel, Ian A. Walmsley, Dagomir Kaszlikowski,
• Abstract summary: Quantum neural networks (QNNs) play an important role as an emerging technology in the rapidly developing field of quantum machine learning. This article reviews the concept of QNNs and their physical realizations, particularly implementations based on quantum optics.
• Score: 3.1935899800030096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: At the dynamic nexus of artificial intelligence and quantum technology, quantum neural networks (QNNs) play an important role as an emerging technology in the rapidly developing field of quantum machine learning. This development is set to revolutionize the applications of quantum computing. This article reviews the concept of QNNs and their physical realizations, particularly implementations based on quantum optics . We first examine the integration of quantum principles with classical neural network architectures to create QNNs. Some specific examples, such as the quantum perceptron, quantum convolutional neural networks, and quantum Boltzmann machines are discussed. Subsequently, we analyze the feasibility of implementing QNNs through photonics. The key challenge here lies in achieving the required non-linear gates, and measurement-induced approaches, among others, seem promising. To unlock the computational potential of QNNs, addressing the challenge of scaling their complexity through quantum optics is crucial. Progress in controlling quantum states of light is continuously advancing the field. Additionally, we have discovered that different QNN architectures can be unified through non-Gaussian operations. This insight will aid in better understanding and developing more complex QNN circuits.

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