论文标题
纠缠提高量子内核对分类效率的作用
The role of entanglement for enhancing the efficiency of quantum kernels towards classification
论文作者
论文摘要
量子内核被认为是说明机器学习中量子计算的好处的潜在资源。考虑到超参数对经典机器学习模型的性能的影响，必须使用量子内核方法识别有希望的超参数以实现量子优势。在这项工作中，我们使用基于线性和完整纠缠的电路作为控制单词之间相关性的超参数来分析和分类文本数据的观点。我们还发现，线性和完整纠缠的使用进一步控制了量子支持向量机（QSVM）的表现力。此外，我们还将提议电路的效率与其他量子电路和经典的机器学习算法进行了比较。我们的结果表明，除了大多数功能的经典算法外，所提出的完全纠缠的电路除了经典算法外，全部或线性纠缠的电路都优于所有其他全部或线性纠缠的电路。实际上，随着功能提高我们提出的完全纠缠模型的效率，也大大增加了。
Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learning model, it is imperative to identify promising hyperparameters using quantum kernel methods in order to achieve quantum advantages. In this work, we analyse and classify sentiments of textual data using a new quantum kernel based on linear and full entangled circuits as hyperparameters for controlling the correlation among words. We also find that the use of linear and full entanglement further controls the expressivity of the Quantum Support Vector Machine (QSVM). In addition, we also compare the efficiency of the proposed circuit with other quantum circuits and classical machine learning algorithms. Our results show that the proposed fully entangled circuit outperforms all other fully or linearly entangled circuits in addition to classical algorithms for most of the features. In fact, as the feature increases the efficiency of our proposed fully entangled model also increases significantly.
