{"ID":2846996,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10653","arxiv_id":"2511.10653","title":"Hybrid Quantum Transformer for Language Generation","abstract":"Although quantum computing has been increasingly applied to replace classical computation, most existing quantum or hybrid models remain confined to simple tasks, with no successful application to large-scale natural language generation to date. In this work, we present the first hybrid quantum-classical large language model (LLM) for natural language generation, HyQuT, capable of performing coherent and context-aware dialogue. The proposed architecture integrates variational quantum circuits (VQCs) into the Transformer framework at both 8M and 150M parameter scales. Experimental results show that a minimal number of qubits (10 qubits with 80 quantum gates) can replace about 10% of the classical parameters in the 150M-parameter model, while achieving comparable convergence stability and generation quality. This study provides an early demonstration of the feasibility of integrating quantum computing to large-scale generative language models.","short_abstract":"Although quantum computing has been increasingly applied to replace classical computation, most existing quantum or hybrid models remain confined to simple tasks, with no successful application to large-scale natural language generation to date. In this work, we present the first hybrid quantum-classical large language...","url_abs":"https://arxiv.org/abs/2511.10653","url_pdf":"https://arxiv.org/pdf/2511.10653v1","authors":"[\"Desheng Kong\",\"Xiangshuo Cui\",\"Jiaying Jin\",\"Jing Xu\",\"Donglin Wang\"]","published":"2025-11-02T10:17:45Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"quant-ph\"]","methods":"[\"Transformer\",\"Large Language Model\",\"Language Model\"]","has_code":false}