While quantum architectures are still under development, when available, they will only be able to process quantum data when machine learning algorithms can only process numerical data. Therefore, in the issues of classification or regression, it is necessary to simulate and study quantum systems that will transfer the numerical input data to a quantum form and enable quantum computers to use the available methods of machine learning. This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network developed for the problem of classification of handwritten digits from the MNIST data set. The comparative results of two models: classical and quantum neural networks of a similar number of training parameters, indicate that the quantum network, although its simulation is time-consuming, overcomes the classical network (it has better convergence and achieves higher training and testing accuracy).
翻译:虽然量子结构尚在开发之中,但只有在机器学习算法只能处理数字数据时,它们才能处理量子数据,因此,在分类或回归问题上,有必要模拟和研究量子系统,将数字输入数据转换成量子形式,使量子计算机能够利用现有的机器学习方法,该材料包括针对MNIST数据集手写数字的分类问题而开发的混合量子古典神经网络的培训和性能实验结果。两种模型的比较结果:具有类似数量培训参数的古典神经网络和量子神经网络,表明量子网络虽然模拟耗时,但克服了古典网络(它具有更好的趋同性,并实现了更高的培训和测试准确性)。