[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-273517-105":59,"doc-detail-273517-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","q-interference-memory-efficient-phase-aware-quantum-inspired-attention","Q-Interference - Memory-Efficient Phase-Aware Quantum-Inspired Attention","","GPT attention estimates token compatibility via dot-product similarity, yielding a simple, effective, memory-efficient mechanism. However, it does not explicitly represent whether strong token features should mutually reinforce or suppress. Q-Interference introduces a fully classical quantum-inspired attention for autoregressive language modeling, attaching amplitude and learned phase to query and key features. Phase-aware scoring combines aligned phases constructively and conflicting phases destructively. An exact trigonometric factorization avoids the large intermediate tensor, enabling stable Transformer integration and a consistent memory advantage over naive phase-aware interference attention on public benchmarks.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/q-interference-memory-efficient-phase-aware-quantum-inspired-attention/273517/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/q-interference-memory-efficient-phase-aware-quantum-inspired-attention/273517.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What limitation of standard dot-product GPT attention does Q-Interference address?","Question",{"text":112,"@type":113},"Standard attention mainly reflects feature similarity and does not explicitly model whether strong token features should reinforce or suppress each other depending on context.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does Q-Interference make attention phase-aware?",{"text":117,"@type":113},"It augments each query and key feature with an amplitude and a learned phase so aligned phases contribute constructively and conflicting phases contribute destructively.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is a naive implementation of phase-aware interference attention impractical?",{"text":121,"@type":113},"It typically requires a large token-pair-feature interaction tensor, making memory usage too high for practical scaling.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},273517,1789471839,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","Q-Interference: Memory-Efficient Phase-Aware Quantum-Inspired Attention  \narXiv : 2608 . 17288v 1 [cs .CL] 18 Aug 2026  \nEmama Nahid  \nKennesaw State University Marietta, GA, USA [enahid@students.kennesaw.edu](enahid@students.kennesaw.edu)  \nTahmid Imtiaz Imu  \nKennesaw State University Marietta, GA, USA [timu1@students.kennesaw.edu](timu1@students.kennesaw.edu)  \nHuayue Gu  \nKennesaw State University Marietta, GA, USA [hgu2@kennesaw.edu](hgu2@kennesaw.edu)  \nLiran Ma  \nMiami University Oxford, OH, USA [mal18@MiamiOH.edu](mal18@MiamiOH.edu)  \nZhipeng Cai  \nGeorgia State University Atlanta, GA, USA [zcai@gsu.edu](zcai@gsu.edu)  \nHonghui Xu  \nKennesaw State University Marietta, GA, USA [hxu10@kennesaw.edu](hxu10@kennesaw.edu)  \nAbstract  \nGPT attention measures token compatibility through dot-product similarity.  \nThis mechanism is simple, effective, and memory-efficient. But it does not explicitly model whether strong token features should reinforce or suppress one another. We introduce Q-Interference, a fully classical quantum-inspired attention mechanism for autoregressive language modeling that augments each query and key feature with an amplitude and a learned phase. The resulting attention score is phase-aware which aligned phases contribute constructively while conflicting phases contribute destructively. Although Q-Interference yields a richer interaction rule than similarity alone, a naive implementation of Q-Interference requires a large token-pair-feature interaction tensor, making it memory-intensive and often impractical. To address this limitation, we propose an exact trigonometric factorization that computes the same score using two standard matrix multiplications avoiding materialization of the large intermediate tensor. Q-Interference fits directly into a Transformer block in GPT and leaves the remainder of the model architecture and next-token prediction objective unchanged. Experiments on public benchmark datasets and baseline models show that the proposed reformulation trains stably in a controlled GPT-style setting and provides a consistent memory advantage over naive phase-aware interference attention. These results support the specific contribution of this work: an exact memory-efficient reformulation that makes phase-aware interference attention practical within a standard GPT pipeline. Our code is available at [https://anonymous.4open.science/r/](https://anonymous.4open.science/r/)[ ](https://anonymous.4open.science/r/)[Q-Interference-Memory-Efficient-Quantum-Inspired-Attention-BDF9](Q-Interference-Memory-Efficient-Quantum-Inspired-Attention-BDF9.)[.](Q-Interference-Memory-Efficient-Quantum-Inspired-Attention-BDF9.)  \n1 Introduction  \nTransformer based GPT models have become a foundation of modern language processing because they can represent context across token sequences Vaswani et al. [2017], Devlin et al. [2019] . A token  \nPreprint.  \nis a basic text unit. Self attention is the mechanism that lets each token compare itself with other tokens in the same sequence. GPT style autoregressive models use causal self attention where each position attends only to previous positions to predict the next token Brown et al. [2020] . However, standard attention usually measures token compatibility through a dot product which mainly reflects feature similarity. This view may miss cases where strong features should support or suppress eachother depending on context.  \nThis challenge becomes more important in long context language modeling Dai et al. [2019] . Practical systems such as document question answering, scientific text modeling and retrieval augmented generation often depend on information spread across many tokens Beltagy et al. [2020], Lewis et al.[2020] . These settings require a model to connect nearby words with distant evidence. Standard dense attention lets each token compare with many earlier tokens. So its memory cost grows with sequence length Dao et al. [2022], Beltagy et al. [2020] . During au","cbCaifywFzolY3vd","https://ap.wps.com/l/cbCaifywFzolY3vd","pdf",1063287,25,"English","# Abstract\n# Introduction","[{\"question\":\"What limitation of standard dot-product GPT attention does Q-Interference address?\",\"answer\":\"Standard attention mainly reflects feature similarity and does not explicitly model whether strong token features should reinforce or suppress each other depending on context.\"},{\"question\":\"How does Q-Interference make attention phase-aware?\",\"answer\":\"It augments each query and key feature with an amplitude and a learned phase so aligned phases contribute constructively and conflicting phases contribute destructively.\"},{\"question\":\"Why is a naive implementation of phase-aware interference attention impractical?\",\"answer\":\"It typically requires a large token-pair-feature interaction tensor, making memory usage too high for practical scaling.\"}]","Q-Interference - Memory-Efficient Phase-Aware Quantum-Inspired Attention | PDF",63]