LFQAP: A Lightweight and Flexible Quantum Artificial Intelligence Application Platform

• Published in: Quantum engineering Vol. 2025; no. 1
• Main Authors: Zhang, Xin, Li, Xiaoyu, Hou, Yuexian
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.01.2025
• Subjects: Algorithms; Artificial intelligence; Back propagation; Circuits; Datasets; Inference; Modularity; Modules; Neural networks; Quantum computers; Quantum computing; Semantic analysis; Semantics; Simulation; Simulators; Software; Tensors
• ISSN: 2577-0470; 2577-0470
• DOI: 10.1155/que2/7359832

Quantum artificial intelligence (AI) is one of the critical research domains in the field of quantum computing and holds significant potential for practical applications in the near future. A quantum AI software platform serves as a fundamental infrastructure for advancing research and facilitating applications in this area. Such a platform supports essential tasks including quantum AI model training, inference, and the deployment of diverse applications. The current quantum AI software platforms prioritize comprehensive functionality; however, they often lack scalability, making it challenging to integrate new features flexibly. Given the broad and evolving research landscape of quantum AI algorithms, it is crucial to develop a software framework that is both user‐friendly and capable of autonomous functional extension. In this paper, we present a lightweight, scalable, and open‐source quantum AI platform designed to support the training and inference of variational quantum algorithms. This platform employs a hierarchical and structured architecture, enhancing the overall manageability and modularity of the software. Notably, it exhibits improved scalability, incorporating a compiler module for the first time. This module enables support for user‐defined quantum devices, including both real physical quantum computers and quantum circuit simulators, as well as custom‐defined optimizers. The platform integrates both tensor network simulator and full‐amplitude simulator, providing powerful ability for quantum AI research. Utilizing these simulators, we conducted training experiments on three publicly available datasets and compared the results with TensorFlow Quantum. The experimental results validate the reliability and effectiveness of our platform, demonstrating its potential as a powerful tool for quantum AI applications.