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一种基于分支多尺度纠缠重整化假设的混合量子-经典分类模型。

A hybrid quantum-classical classification model based on branching multi-scale entanglement renormalization ansatz.

作者信息

Hou Yan-Yan, Li Jian, Xu Tao, Liu Xin-Yu

机构信息

College of Information Science and Engineering, ZaoZhuang University, Zaozhuang, 277160, China.

School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China.

出版信息

Sci Rep. 2024 Aug 9;14(1):18521. doi: 10.1038/s41598-024-69384-6.

DOI:10.1038/s41598-024-69384-6
PMID:39122811
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11316021/
Abstract

Tensor networks are emerging architectures for implementing quantum classification models. The branching multi-scale entanglement renormalization ansatz (BMERA) is a tensor network known for its enhanced entanglement properties. This paper introduces a hybrid quantum-classical classification model based on BMERA and explores the correlation between circuit layout, expressiveness, and classification accuracy. Additionally, we present an autodifferentiation method for computing the cost function gradient, which serves as a viable option for other hybrid quantum-classical models. Through numerical experiments, we demonstrate the accuracy and robustness of our classification model in tasks such as image recognition and cluster excitation discrimination, offering a novel approach for designing quantum classification models.

摘要

张量网络是用于实现量子分类模型的新兴架构。分支多尺度纠缠重整化假设（BMERA）是一种以其增强的纠缠特性而闻名的张量网络。本文介绍了一种基于BMERA的混合量子 - 经典分类模型，并探讨了电路布局、表现力和分类精度之间的相关性。此外，我们提出了一种用于计算成本函数梯度的自动微分方法，这为其他混合量子 - 经典模型提供了一种可行的选择。通过数值实验，我们证明了我们的分类模型在图像识别和簇激发判别等任务中的准确性和鲁棒性，为设计量子分类模型提供了一种新方法。

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本文引用的文献

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Low-Depth Gradient Measurements Can Improve Convergence in Variational Hybrid Quantum-Classical Algorithms.低深度梯度测量可改善变分混合量子-经典算法中的收敛性。
Phys Rev Lett. 2021 Apr 9;126(14):140502. doi: 10.1103/PhysRevLett.126.140502.
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Phys Rev Lett. 2008 Sep 12;101(11):110501. doi: 10.1103/PhysRevLett.101.110501.