Entangled Datasets for Quantum Machine Learning

TL;DR

This paper introduces entangled quantum datasets, like NTangled, to better benchmark quantum machine learning models, emphasizing the importance of quantum data for achieving quantum advantage over classical datasets.

Contribution

It presents the NTangled dataset of quantum states with varying entanglement, and demonstrates how quantum neural networks can generate and utilize these states for benchmarking QML tasks.

Findings

Quantum neural networks can generate the NTangled dataset.

Quantum datasets can improve benchmarking of QML models.

A novel method for generating multipartite entangled states is proposed.

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.