  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 $O(\text{polylog} (M))$ 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.
