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

2025-08-01

AI Summary

The impact of data encoding strategies—specifically angle encoding versus amplitude encoding—and the choice of single-qubit rotation gates (RX, RY, RZ) on the classification accuracy of Variational Quantum Classifiers (VQCs) remains poorly quantified. Method: We systematically evaluate these factors across Wine and Diabetes datasets using a unified quantum circuit architecture and classical optimization algorithms to train multiple VQC instances. Contribution/Results: Our empirical study reveals that the combination of encoding strategy and rotation gate type constitutes a critical hyperparameter pair, with performance differences reaching up to 41% in classification accuracy; optimal selection yields over 30% improvement. This work provides the first quantitative evidence of the dominant role of encoding design in VQC performance, establishing concrete empirical guidance for data embedding paradigm selection in quantum machine learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Evaluating encoding strategies impact on quantum model accuracy
• Comparing Angle and Amplitude encoding in Quantum Machine Learning
• Analyzing rotational gates effect on classification performance

Innovation

• Compares Angle and Amplitude encoding strategies
• Evaluates rotational gates' impact on accuracy
• Identifies embedding as VQC hyperparameter