{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T05:09:34Z","timestamp":1773032974088,"version":"3.50.1"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":230,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472072"],"award-info":[{"award-number":["62472072"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quantum Engineering"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Quantum artificial intelligence (QAI) has made significant progress in recent years. However, the lack of platforms for large\u2010scale QAI models training has constrained research on related algorithms, such as quantum large language models. In this paper, we introduce LFQAP2, a supercomputing\u2010based platform designed for training large\u2010scale 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\u2010internal computing efficiency and excellent scalability, and can reduce internode communication overhead, limiting it to the exchange of only a small number of floating\u2010point values. LFQAP2 is implemented using the MPI\u2009+\u2009OpenMP parallel programming model and deployed on a 16\u2010node cluster, where each node is equipped with 32 computing cores. To assess the platform\u2019s efficiency, we evaluate its performance on two types of tasks: image recognition (MNIST dataset classification) and natural language processing (word embedding). For the 16\u2010qubit MNIST classification task with 128 trainable parameters, the 32\u2010thread acceleration efficiency reaches 0.852, while the 16\u2010node multiprocess acceleration efficiency is 0.357. For the 9\u2010qubit word embedding task with 288 trainable parameters, the 32\u2010thread acceleration efficiency also reaches 0.843, and the 16\u2010node multiprocess acceleration efficiency is 0.598. We successfully completed the training of a 288\u2010parameter model within 23\u2009min, providing a robust computational platform to support research on large\u2010scale quantum models.<\/jats:p>","DOI":"10.1155\/que2\/5577635","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T06:33:18Z","timestamp":1755671598000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LFQAP2: Large\u2010Scale Quantum Artificial Intelligence Training Platform Based on Supercomputing"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7947-916X","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6586-4705","authenticated-orcid":false,"given":"Xiaoyu","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1533-1550","authenticated-orcid":false,"given":"Lianfu","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8202-7447","authenticated-orcid":false,"given":"Qinsheng","family":"Zhu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5546-0205","authenticated-orcid":false,"given":"Geng","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3193-0676","authenticated-orcid":false,"given":"Wenjie","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5079-1110","authenticated-orcid":false,"given":"Lianhui","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3238-493X","authenticated-orcid":false,"given":"Yuexian","family":"Hou","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41567-021-01287-z"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42005-023-01290-1"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-023-04095-x"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1103\/physrevlett.128.080506"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-32550-3"},{"key":"e_1_2_10_6_2","unstructured":"BarronA. R.andKlusowskiJ. M. Approximation and Estimation for High-Dimensional Deep Learning Networks 2018 https:\/\/arxiv.org\/abs\/1809.03090."},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1103\/physrevlett.121.040502"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aav2761"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41534-021-00503-1"},{"key":"e_1_2_10_10_2","unstructured":"Javadi-AbhariA. TreinishM. KrsulichK.et al. Quantum Computing With Qiskit 2024 https:\/\/arxiv.org\/abs\/2405.08810."},{"key":"e_1_2_10_11_2","unstructured":"BroughtonM. VerdonG. McCourtT.et al. A Software Framework for Quantum Machine Learning 2020 https:\/\/arxiv.org\/abs\/2003.02989."},{"key":"e_1_2_10_12_2","unstructured":"XuX. CuiJ. CuiZ.et al. Mindspore Quantum: A User-Friendly Highperformance and Ai-Compatible Quantum Computing Framework 2024 https:\/\/arxiv.org\/abs\/2406.17248."},{"key":"e_1_2_10_13_2","unstructured":"DouM. ZouT. FangY.et al. Qpanda: High-Performance Quantum Computing Framework for Multiple Application Scenarios 2022 https:\/\/arxiv.org\/abs\/2212.14201."},{"key":"e_1_2_10_14_2","unstructured":"VaswaniA. ShazeerN. ParmarN.et al. Attention Is all You Need Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917) 2017 Long Beach."},{"key":"e_1_2_10_15_2","doi-asserted-by":"crossref","unstructured":"MehtaN. TeruelM. SanzP. F. DengX. AwadallahA. H. andKiselevaJ. Improving Grounded Language Understanding in a Collaborative Environment by Interacting With Agents Through Help Feedback Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024 2024 St. Julian\u2019s Malta.","DOI":"10.18653\/v1\/2024.findings-eacl.87"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3564269"},{"key":"e_1_2_10_17_2","unstructured":"BidermanS. SchoelkopfH. AnthonyQ.et al. Oskar Van Der Wal. Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling Proceedings of the 40th International Conference on Machine Learning 2023 Honolulu."},{"key":"e_1_2_10_18_2","unstructured":"OuyangL. WuJ. JiangX.et al. Training Language Models to Follow Instructions With Human Feedback Proceedings of the NIPS\u201922: Proceedings of the 36th International Conference on Neural Information Processing Systems 2022 New Orleans."},{"key":"e_1_2_10_19_2","unstructured":"Manuel Zambrano ChavesJ. WangE. TuT.et al. Tx-llm: A Large Language Model for Therapeutics 2024 https:\/\/arxiv.org\/abs\/2406.06316."},{"key":"e_1_2_10_20_2","unstructured":"FuteralM. ZebazeA. SuarezP. O.et al. Moscar: A Large-Scale Multilingual and Multimodal Document-Level Corpus 2024 https:\/\/arxiv.org\/abs\/2406.08707."},{"key":"e_1_2_10_21_2","unstructured":"EvansE. N. CookM. BradshawZ. P. andLaBordeM. L. Learning With Sasquatch: A Novel Variational Quantum Transformer Architecture With Kernel-Based Self-Attention 2024 https:\/\/arxiv.org\/abs\/2403.14753."},{"key":"e_1_2_10_22_2","doi-asserted-by":"crossref","unstructured":"NguyenX.-B. NguyenH.-Q. Yen-Chi ChenS. KhanS. U. ChurchillH. andQclusformerK. L. A Quantum Transformer-Based Framework for Unsupervised Visual Clustering 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) 2024 Montreal Canada.","DOI":"10.1109\/QCE60285.2024.10304"},{"key":"e_1_2_10_23_2","unstructured":"XueC. ChenZ.-Y. ZhuangXi-N.et al. End-To-End Quantum Vision Transformer: Towards Practical Quantum Speedup in Large-Scale Models 2024 https:\/\/arxiv.org\/abs\/2402.18940."},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3879-7"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.22331\/q-2024-02-22-1265"},{"key":"e_1_2_10_26_2","unstructured":"KhatriN. MatosG. CoopmansL. andClarkS. Quixer: A Quantum Transformer Model 2024 https:\/\/arxiv.org\/abs\/2406.04305."},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1155\/que2\/7359832"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2018.11.005"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1103\/physreva.104.032603"},{"key":"e_1_2_10_30_2","doi-asserted-by":"crossref","unstructured":"LiuY. A. LiuX. L. LiF. N.et al. Closing the \u201cQuantum Supremacy\u201d Gap: Achieving Real-Time Simulation of a Random Quantum Circuit Using a New Sunway Supercomputer Proceedings of the SC21: International Conference for High Performance Computing Networking Storage and Analysis 2021 St. Louis.","DOI":"10.1145\/3458817.3487399"},{"key":"e_1_2_10_31_2","unstructured":"DiederikP. K.andJimmyB. Adam: A Method for Stochastic Optimization 2014 https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1103\/physreva.99.032331"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1137\/050644756"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0208510"},{"key":"e_1_2_10_35_2","unstructured":"https:\/\/huggingface.co\/datasets\/fka\/awesome-chatgpt-prompts\/blob\/main\/prompts.csv."}],"container-title":["Quantum Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/que2\/5577635","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/que2\/5577635","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/que2\/5577635","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:20:00Z","timestamp":1773030000000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/que2\/5577635"}},"subtitle":[],"editor":[{"given":"Guo-Xing","family":"Miao","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/que2\/5577635"],"URL":"https:\/\/doi.org\/10.1155\/que2\/5577635","archive":["Portico"],"relation":{},"ISSN":["2577-0470","2577-0470"],"issn-type":[{"value":"2577-0470","type":"print"},{"value":"2577-0470","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-03-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5577635"}}