Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy

TL;DR

This paper compares amplitude and angle encoding strategies in variational quantum classifiers, showing how the choice of rotational gates significantly affects classification accuracy on real datasets.

Contribution

It provides a systematic analysis of encoding strategies and rotational gates in VQC models, highlighting their impact on performance and hyperparameter importance.

Findings

Accuracy differences between models range from 10% to 41%.

Encoding choice and rotational gates significantly influence classification performance.

Embedding acts as a hyperparameter affecting model accuracy.

Abstract

Recent advancements in Quantum Computing and Machine Learning have increased attention to Quantum Machine Learning (QML), which aims to develop machine learning models by exploiting the quantum computing paradigm. One of the widely used models in this area is the Variational Quantum Circuit (VQC), a hybrid model where the quantum circuit handles data inference while classical optimization adjusts the parameters of the circuit. The quantum circuit consists of an encoding layer, which loads data into the circuit, and a template circuit, known as the ansatz, responsible for processing the data. This work involves performing an analysis by considering both Amplitude- and Angle-encoding models, and examining how the type of rotational gate applied affects the classification performance of the model. This comparison is carried out by training the different models on two datasets, Wine and Diabetes, and evaluating their performance. The study demonstrates that, under identical model topologies, the difference in accuracy between the best and worst models ranges from 10% to 30%, with differences reaching up to 41%. Moreover, the results highlight how the choice of rotational gates used in encoding can significantly impact the model's classification performance. The findings confirm that the embedding represents a hyperparameter for VQC models.