[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122638-en":3,"doc-seo-122638-105":30,"detail-sidebar-cat-0-en-105":91},{"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,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122638,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","CQural - A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning","Training machine learning models incrementally is essential for efficient progress toward artificial general intelligence, mirroring lifelong human learning. Yet conventional neural networks suffer from catastrophic forgetting in continual learning. Existing methods reduce forgetting largely without changing model architecture. This work demonstrates that a hybrid classical-quantum neural network can mitigate catastrophic forgetting and identifies key features for classification, using explanations to improve performance and steer learning away from the decision boundary on MNIST and CIFAR-10.","CQural: A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning  \nSanyam Jain, Indian Institute of Technology, Jodhpur, India 342037  \nAbstract—Training machine learning models in an incremental fashion is not only important but also an efficient way to achieve artificial general intelligence. The ability that humans possess of continuous or lifelong learning helps them to not forget previously learned tasks. However, current neural network models are prone to catastrophic forgetting when it comes to continual learning. Many researchers have come up with several techniques in order to reduce the effect of forgetting from neural networks, however, all techniques are studied classically with a very less focus on changing the machine learning model architecture. In this research paper, we show that it is not only possible to circumvent catastrophic forgetting in continual learning with novel hybrid classicalquantum neural networks, but also ex-plains what features are most important to learn for classification. In addition, we also claim that if the model is trained with these explanations, it tends to give better performance and learn specific features that are far from the decision boundary. Finally, we present the experimental results to show comparisons between classical and classical-quantum hybrid architectures on benchmark MNISTand CIFAR-10 datasets. After successful runs of learning procedure, we found hybrid neural network outperforms classical one in terms of remembering the right evidences of the class-specific features.  \nIndex Terms—Continual Learning, Catastrophic Forgetting, Quantum Machine Learning, and Explainability  \nI. INTRODUCTION  \nQ UANTUM Machine Learning (QML) has gained ex  \nponential exposure after 2019 when Google announced quantum supremacy [1] . Modern-day Machine Learning and Quantum Computing evolved parallelly, however, an interdisciplinary approach has gained much interest to combine Quantum Computing to enhance and speed up classical machine learning to increase the efficiency of the training algorithm.  \nQuantum Deep Learning (part of QML) has embarked on an interesting place in the research community. Previous work  \n[2] in QML has claimed to gain quadratic and logarithmic speedups for traditional ML algorithms and has also shown to achieve quadratic and logarithmic speedups for traditional ML algorithms. Some existing algorithms such as Quantum Bayesian Inference [3], Quantum Perceptron Algorithm [4], Quantum Boltzmann Machine [5], and Quantum SVM [6] . Modern machine learning models tend to be resource-intensive to find complex decision boundaries over an input dataset. As  \nS. Jain is student with the Interdisciplinary Research Programme in Quantum Information & Computation, Indian Institute of Technology, Jodhpur, India, 342037. E-mail: [sanyam.1@iitj.ac.in](sanyam.1@iitj.ac.in)  \ncomplexity of the features in the dataset increases, models take more resources to learn those features.  \nFigure 1 CQural Architecture  \nContinual learning [7] is an advanced incremental online learning theory driven by the idea of lifelong learning. In other words, a learning paradigm is applied for a continuously learning machine learning model. In this particular research, many challenges have to be encountered. One of the prominent and popular defects in continual learning is catastrophic forgetting. Catastrophic forgetting is a tendency of Deep Neural Networks to forget previously learned tasks when they are presented with new tasks. In other words, some examples on which the model is less confident and changes its prediction labels with the course of multiple learning stages are called forgotten examples. Previous research [8] had been done in this line of work tells about the theoretical and statistical proofs that reduce the chances of forgetting events. In addition, a recent work [9] based on calculating the label shifts (label-dispersion) helps investigate the change in labels and ","cbCaif9eK2OHXXZE","https://ap.wps.com/l/cbCaif9eK2OHXXZE","pdf",934555,1,10,"English","en",105,"# Abstract\n# Index Terms\n# Introduction\n## Quantum Machine Learning Background\n## Continual Learning and Catastrophic Forgetting\n## Prior Work and Proposed Approaches","[{\"question\":\"Why is continual learning important in the context of building AI?\",\"answer\":\"Continual learning enables models to acquire knowledge incrementally over time, aligning with the lifelong learning ability that helps humans retain previously learned tasks.\"},{\"question\":\"What problem does the paper focus on in continual learning?\",\"answer\":\"The paper focuses on catastrophic forgetting, where deep neural networks lose previously learned tasks when new tasks are introduced.\"},{\"question\":\"How does the proposed approach compare classical and classical-quantum hybrid architectures?\",\"answer\":\"Experiments on MNIST and CIFAR-10 compare classical versus classical-quantum hybrid architectures, showing improved ability to remember class-specific evidences in the hybrid model.\"}]","CQural - A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning | PDF",1785811856,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cqural-a-novel-cnn-based-hybrid-architecture-for-quantum-continual-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cqural-a-novel-cnn-based-hybrid-architecture-for-quantum-continual-machine-learning/122638/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is continual learning important in the context of building AI?","Question",{"text":75,"@type":76},"Continual learning enables models to acquire knowledge incrementally over time, aligning with the lifelong learning ability that helps humans retain previously learned tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the paper focus on in continual learning?",{"text":80,"@type":76},"The paper focuses on catastrophic forgetting, where deep neural networks lose previously learned tasks when new tasks are introduced.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach compare classical and classical-quantum hybrid architectures?",{"text":84,"@type":76},"Experiments on MNIST and CIFAR-10 compare classical versus classical-quantum hybrid architectures, showing improved ability to remember class-specific evidences in the hybrid model.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]