arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2102.01828quant-phcs.LG
收藏

用ZX演算分析量子神经网络训练中的贫瘠高原现象

Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus

Academy of Mathematics and Systems Science, Chinese Academy of Sciences(中国科学院数学与系统科学研究院);University of Chinese Academy of Sciences(中国科学院大学)

Chen Zhao, Xiao-Shan Gao

摘要:

本文提出了一个通用的方案来分析梯度消失现象,也被称为贫瘠的高原现象,在训练量子神经网络与ZX演算。更准确地说,我们在一定的合理假设下,将荒芜平台定理从酉2-设计电路推广到任何参数化的量子线路。本文的主要技术贡献是将某些积分表示为ZX-图,并用ZX-演算计算它们。用该方法分析了四个具体的不同结构的量子神经网络。结果表明,对于硬件有效的ananalogue和MPS启发的analogue,存在贫瘠高原,而对于QCNN analogue和树张量网络analogue,不存在贫瘠高原。

英文摘要:

In this paper, we propose a general scheme to analyze the gradient vanishing phenomenon, also known as the barren plateau phenomenon, in training quantum neural networks with the ZX-calculus. More precisely, we extend the barren plateaus theorem from unitary 2-design circuits to any parameterized quantum circuits under certain reasonable assumptions. The main technical contribution of this paper is representing certain integrations as ZX-diagrams and computing them with the ZX-calculus. The method is used to analyze four concrete quantum neural networks with different structures. It is shown that, for the hardware efficient ansatz and the MPS-inspired ansatz, there exist barren plateaus, while for the QCNN ansatz and the tree tensor network ansatz, there exists no barren plateau.

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。

↑