Zhao Wenlin, Wang Yinuo, Qu Yingjie, Ma Hongyang, Wang Shumei
School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China.
School of Science, Qingdao University of Technology, Qingdao 266520, China.
Entropy (Basel). 2022 Dec 6;24(12):1783. doi: 10.3390/e24121783.
We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classification task can solve the problem of the classical classifier, this paper proposes a binary quantum neural network classifical model based on an optimized Grover algorithm based on partial diffusion. Trial and error is adopted to extend the partial diffusion quantum search algorithm with the known proportion of target solutions to the unknown state, and to apply the characteristics of the supervised learning of the quantum neural network to binary classify the classified data. Experiments show that the proposed method can effectively retrieve quantum states with similar features. The test accuracy of BQM retrieval under the depolarization noise at the 20th period can reach 97% when the depolarization rate is 0.1. It improves the retrieval accuracy by about 4% and 10% compared with MSE and BCE in the same environment.
我们关注格罗弗算法不适用于目标解比例完全未知的问题。考虑到当前量子神经网络（QNN）用于完成分类任务的现有量子分类器是否能解决经典分类器的问题，本文提出了一种基于部分扩散优化格罗弗算法的二值量子神经网络分类模型。采用试错法将已知目标解比例的部分扩散量子搜索算法扩展到未知状态，并将量子神经网络的监督学习特性应用于对分类数据进行二值分类。实验表明，所提方法能有效检索具有相似特征的量子态。当去极化率为0.1时，第20个周期去极化噪声下BQM检索的测试准确率可达97%。在相同环境下，与MSE和BCE相比，检索准确率提高了约4%和10%。
