[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116873-en":3,"doc-seo-116873-105":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":4,"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},116873,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Improved Financial Forecasting via Quantum Machine Learning","Quantum machine learning offers tools to strengthen machine learning performance across diverse domains. This work presents quantum-enabled approaches for financial forecasting. It first integrates classical and quantum Determinantal Point Processes into Random Forest churn prediction, improving precision by nearly 6%. Second, it develops quantum neural network architectures with orthogonal and compound layers for credit risk assessment, achieving accuracy comparable to classical methods while using significantly fewer parameters. Results support quantum-inspired and future quantum hardware potential.","arXiv :2306 . 12965v1 [ q-fin . ST] 31 May 2023  \nImproved Financial Forecasting via Quantum Machine Learning  \nSohum Thakkar 1 , Skander Kazdaghli 1 , Natansh Mathur 1,2 , Iordanis Kerenidis 1,2 , André J.  \nFerreira–Martins3 , and Samurai Brito3  \n1 QC Ware Corp, Palo Alto,USA and Paris,France  \n2 IRIF, Université Paris Cité and CNRS, France  \n3 Itaú Unibanco, São Paulo, Brazil  \nMay 2023  \nAbstract  \nQuantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6% . Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantuminspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.  \n1 Introduction  \nQuantum computing is a rapidly evolving field that promises to revolutionize various domains, and finance is no exception. There is a variety of computationally hard financial problems for which quantum algorithms can potentially offer advantages [24, 16, 39, 6], for example in combinatorial optimization [34, 42], convex optimization [30, 43], monte carlo simulations [15, 44, 21], and machine learning [41, 18, 1] .  \nIn this work, we explore the potential of quantum machine learning methods in improving the performance of forecasting in finance, specifically focusing on two use cases within the business of Itaú Unibanco, the largest bank in Latin America.  \nIn the first use case, we aim to improve the performance of Random Forest methods for churn prediction. We introduce quantum algorithms for Determinantal Point Processes (DPP) sampling [29], and develop a method of DPP sampling to enhance Random Forest models. We evaluate our model on the churn dataset using classical DPP sampling algorithms and perform experiments on a scaled-down version of the dataset using quantum algorithms. Our results demonstrate that, in the classical setting, the proposed algorithms outperform the baseline Random Forest in precision, efficiency, and bottom line, and also offer a precise understanding of how quantum computing can impact this kind of problem in the future. The quantum algorithm run on an IBM quantum processor gives similar results as the classical DPP on small batch dimensions but falters as the dimensions grow bigger due to hardware noise.  \nIn the second use case, we aim to explore the performance of neural network models for credit risk assessment by incorporating ideas from quantum compound neural networks [33] . We start by using quantum orthogonal neural networks [33], which add the property of orthogonality for the trained model weights to avoid redundancy in the learned features [3] . These orthogonal layers, which can be trained efficiently on a classical computer, are the simplest case of what we call compound neural networks, which explore an exponential space in a structured way. For our use case, we design compound neural network architectures  \nthat are appropriate for financial data. We evaluate their performance on a real-world dataset and show that the quantum compound neural network models both have far fewer parameters and achieve better accuracy and generalization than classical fully-connected neural networks.  \nThis paper is organized as follows: In section 2, we focus on the churn prediction use case and present the DPP-based quantum machine learning methods. In section 3, we present quantum neural network models for risk assessment. Finally, in section 4, we conclude the paper and discuss potential future research directions.  ","cbCaisL0PfGmWG43","https://ap.wps.com/l/cbCaisL0PfGmWG43","pdf",2143004,1,26,"English","en",105,"# Introduction\n## Churn prediction with DPP-enhanced Random Forest\n## Credit risk assessment with quantum-inspired neural networks\n# Conclusion","[{\"question\":\"What is the main goal of this paper?\",\"answer\":\"To improve financial forecasting performance using quantum machine learning methods, focusing on churn prediction and credit risk assessment.\"},{\"question\":\"How does the paper improve churn prediction models?\",\"answer\":\"It enhances Random Forest using Determinantal Point Processes, applying both classical and quantum DPP sampling to improve precision.\"},{\"question\":\"What advantage do the proposed credit risk neural networks provide?\",\"answer\":\"They use orthogonal and compound-layer architectures that match classical performance while requiring far fewer parameters and improving accuracy and generalization.\"}]","Improved Financial Forecasting via Quantum Machine Learning | PDF",1785672168,66,{"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},"improved-financial-forecasting-via-quantum-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/improved-financial-forecasting-via-quantum-machine-learning/116873/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this paper?","Question",{"text":75,"@type":76},"To improve financial forecasting performance using quantum machine learning methods, focusing on churn prediction and credit risk assessment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper improve churn prediction models?",{"text":80,"@type":76},"It enhances Random Forest using Determinantal Point Processes, applying both classical and quantum DPP sampling to improve precision.",{"name":82,"@type":73,"acceptedAnswer":83},"What advantage do the proposed credit risk neural networks provide?",{"text":84,"@type":76},"They use orthogonal and compound-layer architectures that match classical performance while requiring far fewer parameters and improving accuracy and generalization.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]