标题： 利用量子极限学习机进行图像分类 显示英文标题

标题： Harnessing Quantum Extreme Learning Machines for image classification

摘要： 对量子机器学习的兴趣由于其潜在的更高效解决方案而日益增长，这些解决方案可以解决经典方法难以处理的问题。 在此背景下，这里介绍的研究工作专注于使用量子机器学习技术进行图像分类任务。 我们利用量子极限学习机，借助量子储层基底提供的丰富特征映射。 我们系统地分析了量子极限学习机过程的不同阶段，从数据集准备到图像最终分类。 特别是，我们测试了不同的编码方式，以及主成分分析、自编码器的使用，以及通过使用不同哈密顿量的量子储层来研究模型的动力学。 我们的结果表明，引入量子储层可以系统地提高分类器的准确性。 此外，虽然不同的编码方式可能导致显著不同的性能， 但具有不同连通度的哈密顿量在相互作用的情况下表现出相同的识别率。 显示英文摘要

摘要： Interest in quantum machine learning is increasingly growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use of quantum machine learning techniques for image classification tasks. We exploit a quantum extreme learning machine by taking advantage of its rich feature map provided by the quantum reservoir substrate. We systematically analyse different phases of the quantum extreme learning machine process, from the dataset preparation to the image final classification. In particular, we have tested different encodings, together with Principal Component Analysis, the use of Auto-Encoders, as well as the dynamics of the model through the use of different Hamiltonians for the quantum reservoir. Our results show that the introduction of a quantum reservoir systematically improves the accuracy of the classifier. Additionally, while different encodings can lead to significantly different performances, Hamiltonians with varying degrees of connectivity exhibit the same discrimination rate, provided they are interacting.