Abstract: In recent years, quantum computing systems have demonstrated their quantum supremacy in specific sampling problems, marking humanity’s entry into the noisy intermediate-scale quantum (NISQ) era. Quantum machine learning (QML) algorithms have garnered significant attention in the field of quantum computing due to their potential to leverage quantum supremacy in solving practical problems of significance. This has made them a prominent and highly relevant topics in quantum computing research. However, efficiently describing and compiling QML algorithms using existing hybrid quantum-classical machine learning frameworks remains a significant challenge, hindering the development of algorithms. This paper addresses this challenge by introducing QTorch, a quantum-classical hybrid machine learning framework. QTorch is constructed by leveraging PyTorch, an open-source classical machine learning framework, in conjunction with a standalone quantum programming language. It incorporates automatic differentiation techniques tailored for real quantum hardware and quantum-classical hybrid machine learning algorithms. Additionally, QTorch introduces parallel training optimization and parameter substitution optimization, two key features designed to enhance time performance. To evaluate the effectiveness of QTorch, a series of experiments were conducted to validate its capabilities and advantages. The results demonstrate that QTorch serves as an efficient platform supporting the development and implementation of quantum-classical hybrid machine learning algorithms, thereby propelling advancements in the field of QML.

Key words: quantum machine learning, variational quantum circuit, noisy intermediate-scale quantum(NISQ), time performance optimization ,