论文标题
基于张量网络和变异量子电路的混合量子古典分类器
Hybrid quantum-classical classifier based on tensor network and variational quantum circuit
论文作者
论文摘要
在嘈杂的中间尺度量子（NISQ）设备上执行量子机学习（QML）的关键步骤是输入数据编码之前的尺寸缩小。传统的原理分析（PCA）和神经网络已被用来执行此任务。但是，通常对经典和量子层进行单独训练。因此，非常需要一个更好地集成两个关键组件的框架。在这里，我们介绍了一个混合模型，该模型结合了量子启发的张量网络（TN）和变异量子电路（VQC）以执行监督的学习任务，从而可以进行端到端培训。我们表明，基于低键尺寸的基于矩阵的基于矩阵状态的TN的性能优于PCA作为功能提取器，以压缩MNIST数据集二进制分类中VQC的输入数据。该体系结构具有很高的适应性，可以在可用时轻松地合并额外的量子资源。
One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding. Traditional principle component analysis (PCA) and neural networks have been used to perform this task; however, the classical and quantum layers are usually trained separately. A framework that allows for a better integration of the two key components is thus highly desirable. Here we introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks, which allows for an end-to-end training. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset. The architecture is highly adaptable and can easily incorporate extra quantum resource when available.
