Compression and dimensionality reduction techniques for Quantum Machnine Learning applications in High Energy Physics

Abstract

Currently available quantum processors are dominated by short coherence time, small number of qubits, and limited connectivity. Application of quantum machine learning techniques require the embedding of classical data in quantum circuits. Data I/O can become a practical challenge that can hinder the advantages of quantum algorithms. In this talk we discuss about the quantum counterpart of Support Vector Machines (namely Quantum SVMs) for the binary classification of High Energy Physics data associated with the production of the Higgs boson. Recent proposals employ a one-feature-to-one-qubit mapping for the encoding of the classical data, prohibiting the simulation of datasets with extensive number of features. This imposes the need of feature compression on complex datasets with the challenge to maintain sufficient information to achieve high classification accuracy. In particular, we implement and compare feature extraction and dimensionality reduction techniques with respect to the quantum machine algorithm and identify the ones that have the minimal effect in the classification accuracy.