Keywords: 
• 量子纠缠 /
• 张量网络 /
• 人工神经网络 /
• 深度学习

Abstract: Quantum entanglement is playing an increasingly significant role in the studies of quantum states of matter. It identifies novel phases and phase transitions beyond the traditional paradigms, and guides efficient simulation of quantum systems using classical computers. Recently, along with the application of deep learning technology to quantum many-body systems, a new perspective on deep learning emerges through the lens of quantum entanglement. Entanglement quantifies the complexity of a real dataset in machine learning and can guide the architecture design of artificial neural networks. Along this line, insights and theories originally developed for quantum many-body systems may find unexpected applications in real-world problems.