Empirical Power of Quantum Encoding Methods for Binary Classification

TL;DR

This paper investigates how different quantum encoding strategies affect binary classification performance on real-world data, comparing them with classical LightGBM, and finds IQP encoding often matches classical results.

Contribution

It provides a comparative analysis of quantum encoding methods for real-world data classification and introduces a quantum annealing-based feature selection approach.

Findings

IQP encoding often yields results statistically equivalent to classical methods.

Quantum encoding strategies can be effective with limited quantum resources.

Feature selection via quantum annealing supports encoding of larger datasets.

Abstract

Quantum machine learning is one of the many potential applications of quantum computing, each of which is hoped to provide some novel computational advantage. However, quantum machine learning applications often fail to outperform classical approaches on real-world classical data. The ability of these models to generalize well from few training data points is typically considered one of the few definitive advantages of this approach. In this work, we will instead focus on encoding schemes and their effects on various machine learning metrics. Specifically, we focus on real-world data encoding to demonstrate differences between quantum encoding strategies for several real-world datasets and the classification model standard, LightGBM. In particular, we apply the following encoding strategies, including three standard approaches and two modified approaches: Angle, Amplitude, IQP, Entangled Angle, and Alternative IQP. As these approaches require either a significant number of qubits or gates to encode larger datasets, we perform feature selection to support the limited computing power of quantum simulators. This feature selection is performed through a quantum annealing enhanced approach that builds on a QUBO formulation of the problem. In this work, we provide a preliminary demonstration that quantum machine learning with the IQP encoding and LightGBM produce statistically equivalent results for a large majority of the assigned learning tasks.