[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119517-en":3,"doc-seo-119517-105":30,"detail-sidebar-cat-0-en-105":92},{"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},119517,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",6,"Technology","AQMLator - An Auto Quantum Machine-Learning E-Platform","AQMLator is an auto quantum machine-learning platform designed to reduce human effort in constructing quantum-enhanced ML models. Successful ML typically depends on training data, model architecture, and training procedure, yet selecting an appropriate quantum model component can be difficult for non-experts. AutoML addresses architecture search, while recent advances in quantum computing have enabled quantum machine learning (QML). AQMLator automatically proposes and trains quantum layers with minimal user input, integrating with standard ML libraries for use in existing ML pipelines.","Computer Science • 26(SI) 2025 [https://doi.org/10.7494/csci.2025.26.SI.7063](https://doi.org/10.7494/csci.2025.26.SI.7063)  \nAbstract  \nKeywords  \nCitation  \nCopyright  \nTomasz Rybotycki Piotr Gawron  \nAQMLATOR – AN AUTO QUANTUM MACHINE-LEARNING E-PLATFORM  \nThe successful implementation of a machine-learning (ML) model requires three main components: a training data set, a suitable model architecture, and a suitable training procedure. Given the data set and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on an automatic architecture search – a meta method that aims to remove the need for human interaction with the ML system-design process. The success of ML and the development of quantum computing (QC) in recent years has led to the birth of a new fascinating field called quantum machine learning (QML), which incorporates quantum computers into ML models (among other things) . In this paper, we present AQMLator, an auto quantum machine-learning platform that aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. In this way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.  \nauto machine learning, quantum computing, quantum machine learning Computer Science 26(SI) 2025: 29–43  \n© 2025 Author(s) . This is an open access publication, which can be used, distributed and reproduced in any medium according to the Creative Commons CC-BY 4.0 License.  \n1. Introduction  \nMachine learning (ML) is one of the fastest-progressing research directions in applied computer science. This field investigates the development of algorithms that can learn from data by fitting a collection of model parameters to the data via the iterative optimization of an objective function. The selection of a model structure (be it a neural network or kernel function) is a problem-dependent task that is often performed by hand; however, auto-ML systems [14] exist that can choose a model automatically depending solely on the input data and the task at hand.  \nQuantum computing (QC) studies how difficult computational problems can be efficiently solved by using quantum mechanics. A large-scale error-corrected quantum computer can solve computational problems that do not have a classical solution. A prime example of this is Shor’s algorithm [30] for integer factorization. The “holy grail” of applied QC is the so-called quantum supremacy or quantum advantage. This is the name for the technological milestone that marks the moment when quantum machines will solve a specific task faster than the most advanced supercomputer. Although there have already been several quantum supremacy claims in recent years [4], there are no practical problems that are solvable by only using quantum computing as of yet.  \nThe search for such practical problems focuses on applications in the soft computing areas that are less susceptible to the current quantum hardware imperfections; one of the possible applications of QC is quantum machine learning (QML) [7] . This field of science investigates how quantum computers can be employed to build ML models that can be fit to data and then be used during the inference process. In oneof the QML scenarios, a variational quantum circuit that forms a quantum neural network (QNN) constitutes only one part of the ML data-processing pipeline. Since designing such a pipeline with a quantum component is challenging for non-experts in QC, we propose an auto-ML solution that suggests ready-to-use QML models.  \nThis paper is organized as follows. In the next section, we present a short overview of the state of the art. We point out the challenges that have been laid before auto(mated) (quantum) machine learning, the most recent techniques that tackle these problems, and the related software. In Section 3 (the main part of this work), we present AQMLator – a","cbCaidKMTX6cgaEm","https://ap.wps.com/l/cbCaidKMTX6cgaEm","pdf",683353,1,15,"English","en",105,"# Introduction\n## Machine learning automation (AutoML)\n## Quantum computing and quantum machine learning\n## Contribution and paper structure\n# State of the art\n## Automated machine learning for complex tasks\n## Differences between quantum and classical ML","[{\"question\":\"What core idea does AQMLator target in building QML models?\",\"answer\":\"AQMLator targets the automatic proposal and training of quantum layers within an ML model, requiring minimal user input while fitting into existing ML workflows.\"},{\"question\":\"Why is automation especially important in quantum machine learning?\",\"answer\":\"QML models differ substantially from classical ones, so standard AutoML approaches may need adjustments; additionally, tasks like quantum architecture search are challenging for non-experts due to quantum-specific constraints.\"},{\"question\":\"How does AQMLator make it easier to use quantum machine learning?\",\"answer\":\"It uses standard ML libraries to fit into existing ML pipelines, lowering the entry barrier for data scientists who want to leverage QML without extensive QC expertise.\"}]","AQMLator - An Auto Quantum Machine-Learning E-Platform | PDF",1785724735,38,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"aqmlator-an-auto-quantum-machine-learning-e-platform","",{"@graph":36,"@context":86},[37,54,69],{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/aqmlator-an-auto-quantum-machine-learning-e-platform/119517/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What core idea does AQMLator target in building QML models?","Question",{"text":76,"@type":77},"AQMLator targets the automatic proposal and training of quantum layers within an ML model, requiring minimal user input while fitting into existing ML workflows.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why is automation especially important in quantum machine learning?",{"text":81,"@type":77},"QML models differ substantially from classical ones, so standard AutoML approaches may need adjustments; additionally, tasks like quantum architecture search are challenging for non-experts due to quantum-specific constraints.",{"name":83,"@type":74,"acceptedAnswer":84},"How does AQMLator make it easier to use quantum machine learning?",{"text":85,"@type":77},"It uses standard ML libraries to fit into existing ML pipelines, lowering the entry barrier for data scientists who want to leverage QML without extensive QC expertise.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,114,119,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]