Learning Reduced Representations for Quantum Classifiers
This work addresses the limitation of quantum machine learning for high-dimensional data, enabling broader applicability to domains like particle physics, though it is incremental as it adapts existing methods.
The paper tackled the problem of applying quantum machine learning to high-dimensional data by using dimensionality reduction methods, showing that autoencoder-based methods, particularly their Sinkclass autoencoder, performed 40% better than baseline on a Higgs boson classification task.
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.