TITLE: Entangled Datasets for Quantum Machine Learning 

ABSTRACT: 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.
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.
Entangled Datasets for Quantum Machine Learning
http://arxiv.org/abs/2109.03400v2