We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model, which significantly reduces the parameter count from M to O(polylog(M)) during training. Our experiments demonstrate QT's effectiveness in classification tasks, offering insights into its potential to revolutionize machine learning by leveraging quantum computational advantages. This approach not only improves model efficiency but also reduces generalization errors, showcasing QT's potential across various machine learning applications.
@article{arxiv.2405.11304,
title = {Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective},
author = {Chen-Yu Liu and En-Jui Kuo and Chu-Hsuan Abraham Lin and Jason Gemsun Young and Yeong-Jar Chang and Min-Hsiu Hsieh and Hsi-Sheng Goan},
journal= {arXiv preprint arXiv:2405.11304},
year = {2024}
}