将量子物理中的张量网络引入机器学习，提升效率与可解释性。
Quantum-inspired tensor networks in machine learning models
- 借鉴量子多体物理的张量网络压缩高维数据依赖关系
- 可作为神经网络组件分解工具，降低计算复杂度
- 适合关注模型效率与可解释性的研究人员
张量网络最初为多体物理中多粒子量子态的压缩表示而设计，通过捕捉最相关的依赖关系缓解多体系统的指数级复杂性。由于量子纠缠与统计相关性在形式上的相似性，张量网络近年来被引入机器学习领域，既作为替代学习架构，也用于神经网络组件的分解。预期其在量子多体物理中建立的理论基础能带来计算效率、可解释性或隐私保护方面的优势。本文综述了张量网络在机器学习中的应用，对当前技术进展、潜在优势及需克服的挑战进行了批判性评估。
原文摘要 · Abstract (English)
Tensor networks were developed in the context of many-body physics as compressed representations of multiparticle quantum states. These representations mitigate the exponential complexity of many-body systems by capturing only the most relevant dependencies. Due to the formal similarity between quantum entanglement and statistical correlations, tensor networks have recently been integrated in machine learning, operating both as alternative learning architectures and as decompositions of components of neural networks. The expectation is that the theoretical understanding of tensor networks developed within quantum many-body physics leads to novel methods that offer advantages in terms of computational efficiency, explainability, or privacy. Here we review the use of tensor networks in the context of machine learning, providing a critical assessment of the state of the art, the potential advantages, and the challenges that must be overcome.
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