Learning Reduced Representations for Quantum Classifiers

TL;DR

This paper explores how various dimensionality reduction techniques, especially autoencoders, can improve the performance of quantum classifiers on complex datasets, exemplified by particle physics data.

Contribution

It introduces a novel autoencoder, Sinkclass autoencoder, that significantly enhances quantum classifier performance on high-dimensional data.

Findings

Autoencoders outperform traditional methods in data reduction.

Sinkclass autoencoder achieves 40% better results than baseline.

Enhanced data representations expand quantum machine learning applicability.

Abstract

Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this impasse is the application of dimensionality reduction methods before passing the data to the quantum algorithm. We investigate six conventional feature extraction algorithms and five autoencoder-based dimensionality reduction models to a particle physics data set with 67 features. The reduced representations generated by these models are then used to train a quantum support vector machine for solving a binary classification problem: whether a Higgs boson is produced in proton collisions at the LHC. We show that the autoencoder methods learn a better lower-dimensional representation of the data, with the method we design, the Sinkclass autoencoder, performing 40% better than the baseline. The methods developed here open up the applicability of quantum machine learning to a larger array of data sets. Moreover, we provide a recipe for effective dimensionality reduction in this context.