QMill: Representative Quantum Data Generation for Quantum Machine Learning Utility

2025-09-25

AI Summary

Quantum machine learning (QML) suffers from a scarcity of authentic quantum training data, while existing synthetic data generation methods fail to faithfully capture essential quantum entanglement properties. To address this, we propose QMill—the first quantum data synthesis framework capable of generating high-fidelity entangled states under low circuit depth. QMill integrates efficient quantum state preparation with a classical-quantum distribution mapping algorithm, enabling the generation of quantum data that preserves both entanglement structure and statistical diversity across representative classical and quantum distributions. Experimental results demonstrate that QMill-synthesized data significantly improves downstream QML models’ training efficiency, generalization performance, and evaluation reliability. By providing physically realistic and task-representative benchmark datasets, QMill advances the development and rigorous assessment of QML models.

Technology Category

• Machine Learning: Quantum Machine Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data generation and simulation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Quantum machine learning (QML) promises significant speedups, particularly when operating on quantum datasets. However, its progress is hindered by the scarcity of suitable training data. Existing synthetic data generation methods fall short in capturing essential entanglement properties, limiting their utility for QML. To address this, we introduce QMill, a low-depth quantum data generation framework that produces entangled, high-quality samples emulating diverse classical and quantum distributions, enabling more effective development and evaluation of QML models in representative-data settings.

Problem

• Generating quantum data with realistic entanglement properties
• Addressing scarcity of suitable training data for QML
• Enabling effective QML model development with representative samples

Innovation

• QMill generates entangled quantum data samples
• Framework emulates diverse classical and quantum distributions
• Enables effective QML model development with representative data