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:量子机器学习已经确立为跨学科领域,以克服古典机器学习和神经网络的局限性。这是一个研究领域,可以证明量子计算机能够解决古典计算机难以使用的投入之间复杂关联的问题。这意味着量子计算机的学习模型对于应用来说可能更强大,有可能更快地计算,对较少的数据进行更宽泛的概括化。本文件的目的是研究如何利用量子神经网络(QNN)的培训,利用量子优化算法提高QNNNN的性能和时间复杂性。一个古典神经网络可以部分量化,以创建主要用于分类和图像识别的混合量子古典神经网络。在本文件中,在将变式参数纳入称为Varational Qantum Neural网络(VQNNNNN)的输入层时,我们把QNNNS的成本功能与在Hilbert空间的超位置的相对阶段相连接。参数可以与QQ的可调和QQVMNMA的精确度近度度精确度精确度(QA)的精确度测试和不及不具有复杂数字级的图像级化(QAAAA的模型) 和不具有较低级化的图像化的QNNNISQA的模拟的图像。</s>