Once developed for quantum theory, tensor networks have been established as a successful machine learning paradigm. Now, they have been ported back to the quantum realm in the emerging field of quantum machine learning to assess problems that classical computers are unable to solve efficiently. Their nature at the interface between physics and machine learning makes tensor networks easily deployable on quantum computers. In this review article, we shed light on one of the major architectures considered to be predestined for variational quantum machine learning. In particular, we discuss how layouts like MPS, PEPS, TTNs and MERA can be mapped to a quantum computer, how they can be used for machine learning and data encoding and which implementation techniques improve their performance.
翻译:一旦用于量子理论,张量网络就被证明是一种成功的机器学习范例。现在,它们已经被移植回量子领域,在新兴的量子机器学习领域中评估经典计算机无法有效解决的问题。它们在物理学和机器学习之间的本质使得张量网络很容易部署在量子计算机上。在这篇综述文章中,我们阐述了一种被认为是用于变分量子机器学习的主要架构。特别地,我们讨论了诸如MPS、PEPS、TTNs和MERA这样的布局如何映射到量子计算机上,它们如何用于机器学习和数据编码以及哪些实现技术提高了它们的性能。