Entangled Datasets for Quantum Machine Learning

High-quality, large-scale datasets 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. While most proposed QML architectures are benchmarked via classical datasets, there are still doubts if QML on classical datasets can achieve 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 states with different amounts and types of multipartite entanglement for up to 12 qubits. We first show how a quantum neural network (QNN) can be trained to generate the states in the dataset. Then, we use the NTangled dataset to benchmark QML models for supervised learning tasks. We also consider an alternative scalable entanglement-based dataset composed of states prepared by circuits of different depths. With QNNs, we demonstrate that high classification accuracies can be achieved for learning the depth of an ansatz. 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.