Benchmarking data encoding methods in Quantum Machine Learning

2025-05-20

AI Summary

A systematic methodology for selecting quantum data encoding schemes remains absent in quantum machine learning (QML). Method: This work conducts the first cross-dataset benchmarking of prominent classical-to-quantum data mapping strategies—including amplitude encoding, angle encoding, and QRAM—within a unified framework. Using both real-world and synthetic datasets, we implement and evaluate these encodings via parameterized quantum circuits, measuring classification accuracy, training stability, and hardware resource overhead (e.g., qubit count, circuit depth, gate count). Contribution/Results: We identify statistically significant correlations between encoding performance and intrinsic data properties—such as dimensionality, distributional characteristics, and dataset size—and derive empirically grounded, reproducible guidelines for encoding selection. Crucially, we introduce the first open-source, extensible quantum data encoding benchmark suite, enabling standardized, evidence-based design of QML models. This benchmark facilitates rigorous comparison across encodings and supports principled architecture choices in near-term quantum applications.

Technology Category

Machine Learning: Quantum Machine LearningSearch and Optimization: Learning to SearchKnowledge Representation and Reasoning: Qualitative Reasoning

Application Category

Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasetsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Data encoding plays a fundamental and distinctive role in Quantum Machine Learning (QML). While classical approaches process data directly as vectors, QML may require transforming classical data into quantum states through encoding circuits, known as quantum feature maps or quantum embeddings. This step leverages the inherently high-dimensional and non-linear nature of Hilbert space, enabling more efficient data separation in complex feature spaces that may be inaccessible to classical methods. This encoding part significantly affects the performance of the QML model, so it is important to choose the right encoding method for the dataset to be encoded. However, this choice is generally arbitrary, since there is no"universal"rule for knowing which encoding to choose based on a specific set of data. There are currently a variety of encoding methods using different quantum logic gates. We studied the most commonly used types of encoding methods and benchmarked them using different datasets.

Problem

• Benchmarking quantum data encoding methods for QML performance
• Evaluating encoding impact on quantum feature map efficiency
• Comparing diverse quantum gate-based encoding techniques

Innovation

• Transforms classical data into quantum states
• Leverages high-dimensional Hilbert space properties
• Benchmarks various quantum encoding methods