High-quality, large-scale datasets have played a crucial role in the development and success of classical machine learning. Quantum Machine Learning (QML) is a new field that aims to use quantum computers for data analysis, with the hope of obtaining a quantum advantage of some sort. While most proposed QML architectures are benchmarked using classical datasets, there is still doubt whether QML on classical datasets will achieve such an advantage. In this work, we argue that one should instead employ quantum datasets composed of quantum states. For this purpose, we introduce the NTangled dataset composed of quantum states with different amounts and types of multipartite entanglement. We first show how a quantum neural network can be trained to generate the states in the NTangled dataset. Then, we use the NTangled dataset to benchmark QML models for supervised learning classification tasks. We also consider an alternative entanglement-based dataset, which is scalable and is composed of states prepared by quantum circuits with different depths. As a byproduct of our results, we introduce a novel method for generating multipartite entangled states, providing a use-case of quantum neural networks for quantum entanglement theory.
翻译:高质量的大型数据集在古典机器学习的发展和成功中发挥了关键作用。 量子机器学习( QML) 是一个新领域, 旨在使用量子计算机进行数据分析, 希望获得某种量子优势 。 虽然大多数拟议的量子计算机结构使用古典数据集进行基准化, 古典数据集上的量子数据集是否具有这种优势仍有疑问 。 在这项工作中, 我们主张应当使用由量子状态组成的量子数据集。 为此, 我们引入由量子体不同数量和不同类型多相缠绕的量子国家组成的NTcle数据集。 我们首先展示一个量子神经网络如何被训练, 以生成在涅茨缠绕数据集中的状态 。 然后, 我们用NTML数据集来为受监管的学习分类任务设定QML模型基准。 我们还考虑一个基于量子数据集的替代纠结点, 该数据集由量子电路组成, 由不同深度的量子电路组成 。 作为我们结果的副产品, 我们为生成量子分子网络提供一种新式的量子网络, 量子网络, 提供了一种新式的量子网络的量子分子网络使用。