LFQAP2: Large‐Scale Quantum Artificial Intelligence Training Platform Based on Supercomputing
Xin Zhang, Xiaoyu Li, L. F. Wei, Qinsheng Zhu, Geng Chen, Wenjie Sun, Lianhui Yu, Yuexian Hou · Quantum Engineering · 2025
Quantum artificial intelligence (QAI) has made significant progress in recent years. However, the lack of platforms for large‐scale QAI models training has constrained research on related algorithms, such as quantum large language models. In this paper, we introduce LFQAP2, a supercomputing‐based platform designed for training large‐scale QAI models. The platform employs a hybrid parallelization strategy that integrates data parallelism and model parallelism. For data parallelism, different threads compute the loss for distinct sample batches. For model parallelism, different processes compute the gradients for separate parameter batches. This parallelization strategy integrates the strengths of both data parallelism and model parallelism. It has high node‐internal computing efficiency and excellent scalability, and can reduce internode communication overhead, limiting it to the exchange of only a small number of floating‐point values. LFQAP2 is implemented using the MPI + OpenMP parallel programming model and deployed on a 16‐node cluster, where each node is equipped with 32 computing cores. To assess the platform’s efficiency, we evaluate its performance on two types of tasks: image recognition (MNIST dataset classification) and natural language processing (word embedding). For the 16‐qubit MNIST classification task with 128 trainable parameters, the 32‐thread acceleration efficiency reaches 0.852, while the 16‐node multiprocess acceleration efficiency is 0.357. For the 9‐qubit word embedding task with 288 trainable parameters, the 32‐thread acceleration efficiency also reaches 0.843, and the 16‐node multiprocess acceleration efficiency is 0.598. We successfully completed the training of a 288‐parameter model within 23 min, providing a robust computational platform to support research on large‐scale quantum models.