用于监督学习的数态保持张量网络分类器
Number-State Preserving Tensor Networks as Classifiers for Supervised Learning
摘要：
我们提出了一个限制类的张量网络状态，建立从数量状态保持张量，监督学习任务。这类张量网络被认为是分类器的自然选择，因为（i）它们将经典数据映射到经典数据，从而保留了张量变换下数据的可解释性，（ii）它们可以有效地训练以最大化其对经典数据集的标量积，（iii）它们在这项任务中似乎与通用（不受限制的）张量网络一样强大。我们的建议是使用各种基准分类问题，其中常用的网络（包括MPS，TTN和MERA）的数量状态保持版本的训练作为有效的分类器。这项工作为强大的张量网络方法（如MERA）开辟了道路，这些方法以前作为分类器在计算上很难处理，可用于图像识别等困难任务。
英文摘要：
We propose a restricted class of tensor network state, built from number-state preserving tensors, for supervised learning tasks. This class of tensor network is argued to be a natural choice for classifiers as (i) they map classical data to classical data, and thus preserve the interpretability of data under tensor transformations, (ii) they can be efficiently trained to maximize their scalar product against classical data sets, and (iii) they seem to be as powerful as generic (unrestricted) tensor networks in this task. Our proposal is demonstrated using a variety of benchmark classification problems, where number-state preserving versions of commonly used networks (including MPS, TTN and MERA) are trained as effective classifiers. This work opens the path for powerful tensor network methods such as MERA, which were previously computationally intractable as classifiers, to be employed for difficult tasks such as image recognition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
