论文标题
使用量子张量网络的量子相识别
Quantum Phase Recognition using Quantum Tensor Networks
论文作者
论文摘要
机器学习（ML）最近促进了解决与多体物理系统有关的问题的许多进步。考虑到这些问题的内在量子性质，自然可以推测量子增强的机器学习将使我们能够比目前更大的细节。有了这种动机，本文研究了一种基于受张量网络启发的较浅的差异ANSATZ，以进行监督的学习任务。特别是，我们首先使用Fashion-Mnist数据集查看标准图像分类任务，并研究重复张量网络层对ANSATZ表达性和性能的效果。最后，我们使用该策略来解决一个和二维的横向场iSing和Heisenberg旋转模型的量子阶段识别问题，在那里我们能够达到$ \ geq 98 \％$ $ $ $ $ $ $测试设置的精确度，并且两个多尺度的纠缠重新统治重新归化Ansatz（mera）和树木Tensor网络（TESOR）网络（TTTN）（TTN）量化了量子。
Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanced machine learning will enable us to unveil even greater details than we currently have. With this motivation, this paper examines a quantum machine learning approach based on shallow variational ansatz inspired by tensor networks for supervised learning tasks. In particular, we first look at the standard image classification tasks using the Fashion-MNIST dataset and study the effect of repeating tensor network layers on ansatz's expressibility and performance. Finally, we use this strategy to tackle the problem of quantum phase recognition for the transverse-field Ising and Heisenberg spin models in one and two dimensions, where we were able to reach $\geq 98\%$ test-set accuracies with both multi-scale entanglement renormalization ansatz (MERA) and tree tensor network (TTN) inspired parametrized quantum circuits.
