Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are able to solve problems with complex correlations between inputs that can be hard for classical computers. This suggests that learning models made on quantum computers may be more powerful for applications, potentially faster computation and better generalization on less data. The objective of this paper is to investigate how training of quantum neural network (QNNs) can be done using quantum optimization algorithms for improving the performance and time complexity of QNNs. A classical neural network can be partially quantized to create a hybrid quantum-classical neural network which is used mainly in classification and image recognition. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs). We encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters. The parameters are tuned with an iterative quantum approximate optimisation (QAOA) mixer and problem hamiltonians. VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets (more complex) which converges the computation in lesser time than QNN with decent training accuracy.

翻译：量子机器学习已发展成为一个跨学科领域，旨在克服经典机器学习与神经网络的局限性。这一研究领域能够证明量子计算机可处理输入间存在复杂关联的问题，而这类问题对经典计算机而言可能较为困难。这表明基于量子计算机构建的学习模型在应用层面更具潜力，可能实现更快的计算速度以及在少量数据上更好的泛化能力。本文旨在探究如何利用量子优化算法训练量子神经网络（QNNs），以提升其性能并降低时间复杂度。通过将经典神经网络部分量子化，可构建混合量子-经典神经网络，主要用于分类与图像识别任务。本文提出一种QNN结构，该结构将变分参数化电路作为输入层，称为变分量子神经网络（VQNNs）。我们将QNN的代价函数编码到网络参数希尔伯特空间中叠加态的相对相位上，并通过迭代量子近似优化（QAOA）混合器与问题哈密顿量对参数进行调优。在MNIST手写数字识别（较简单）与裂纹图像分类（较复杂）数据集上的实验表明，VQNNs能以更短的计算时间收敛，并保持较高的训练精度。