[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31577":3,"doc-seo-31577":27},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31577,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction","Multi-agent frameworks powered by Large Language Models (LLMs) can generate code via test-driven development, yet their benefits for domain-specific languages remain underexplored. This paper introduces a multi-agent quantum programming framework that targets quantum-design optimizations, especially quantum error correction. Separate agents iteratively refine code using a semantic analyzer with multi-pass inference and an error-correction decoder. Experiments compare structured Chain-of-Thought and Retrieval-Augmented Generation, showing up to 50% gains from structured CoT while RAG improves accuracy by only 4%.","cbCaisaHh0bfqoXc","https://ap.wps.com/l/cbCaisaHh0bfqoXc","pdf",462309,1,7,"English","en","# Introduction\n# Multi-Agent Quantum Code Generation Framework\n## Quantum error correction integration\n## Multi-pass semantic inference and decoding\n# Evaluation and Results\n## Comparison of CoT and RAG\n## Test suite for optimization impact","[{\"question\":\"What problem does the paper address in LLM-assisted quantum code generation?\",\"answer\":\"It addresses the limited effectiveness of multi-agent LLM code generation for domain-specific quantum programs, where optimizations like quantum error correction are essential for fault-tolerant code.\"},{\"question\":\"How does the proposed multi-agent framework improve quantum code accuracy?\",\"answer\":\"Each agent focuses on distinct optimizations and iteratively refines generated code using a semantic analyzer with multi-pass inference together with an error correction code decoder.\"},{\"question\":\"What do the experiments show about Chain-of-Thought and Retrieval-Augmented Generation for quantum programming?\",\"answer\":\"Structured Chain-of-Thought significantly improves generation quality by up to 50%, while Retrieval-Augmented Generation yields only limited improvement, about a 4% accuracy increase.\"}]",1779742918,18,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"enhancing-llm-based-quantum-code-generation-with-multi-agent-optimization-and-quantum-error-correction","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/enhancing-llm-based-quantum-code-generation-with-multi-agent-optimization-and-quantum-error-correction/31577/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-25",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What problem does the paper address in LLM-assisted quantum code generation?","Question",{"text":73,"@type":74},"It addresses the limited effectiveness of multi-agent LLM code generation for domain-specific quantum programs, where optimizations like quantum error correction are essential for fault-tolerant code.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does the proposed multi-agent framework improve quantum code accuracy?",{"text":78,"@type":74},"Each agent focuses on distinct optimizations and iteratively refines generated code using a semantic analyzer with multi-pass inference together with an error correction code decoder.",{"name":80,"@type":71,"acceptedAnswer":81},"What do the experiments show about Chain-of-Thought and Retrieval-Augmented Generation for quantum programming?",{"text":82,"@type":74},"Structured Chain-of-Thought significantly improves generation quality by up to 50%, while Retrieval-Augmented Generation yields only limited improvement, about a 4% accuracy increase.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]