Overfitting in quantum machine learning and entangling dropout
Overfitting in quantum machine learning and entangling dropout
复制标题
量子机器学习中的过度拟合和纠缠丢失
DOI：
10.1007/s42484-022-00087-9
复制
发表时间：
2022
影响因子：
4.8
通讯作者：
Naoki Yamamoto
中科院分区：
文献类型：
--
作者：
Masahiro Kobayashi;Kouhei Nakaji;Naoki Yamamoto
机器学习的最终目标是基于给定的训练数据集构建一个对未见过的数据集具有泛化能力的模型函数。如果模型函数具有太多的表达能力，那么它可能会过度拟合训练数据，从而失去泛化能力。为了避免这种过度拟合问题，经典机器学习体系中开发了多种技术，而 dropout 就是其中一种有效的方法。本文提出了量子机器学习领域中这种技术的直接模拟，即纠缠丢失，这意味着在训练过程中随机删除给定参数化量子电路中的一些纠缠门，以降低电路的可表达性。一些简单的案例研究表明该技术实际上抑制了过度拟合。
The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model function has too much expressibility power, then it may overfit to the training data and as a result lose the generalization capability. To avoid such overfitting issue, several techniques have been developed in the classical machine learning regime, and the dropout is one such effective method. This paper proposes a straightforward analogue of this technique in the quantum machine learning regime, the entangling dropout, meaning that some entangling gates in a given parametrized quantum circuit are randomly removed during the training process to reduce the expressibility of the circuit. Some simple case studies are given to show that this technique actually suppresses the overfitting.
影响因子：
5.1
作者：
Nayak, Suraj K.;Pradhan, Bikash K.;Pal, Kunal
通讯作者：
Pal, Kunal