前沿量子科技研究中心量子資訊專題演講
Quantum Train: A Novel Hybrid Quantum-Classical Machine Learning Approach in the Model Compression Perspective
We introduce 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.