[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116867-en":3,"doc-seo-116867-105":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},116867,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","VQE-generated Quantum Circuit Dataset for Machine Learning","Quantum machine learning may surpass classical approaches, yet its practical advantage remains unclear for common datasets. This work targets learning from quantum data—specifically quantum circuits—by proposing a real-world task: clustering and classification of circuits. The paper introduces an elementary dataset generated by the variational quantum eigensolver, built from six condensed-matter Hamiltonian types (4–20 qubits) and ten ansätze with depths 3–32, forming six classes with 300 samples each. Quantum methods can learn the dataset efficiently, including successful classification using real 4-qubit IBMQ devices.","arXiv :2302 .09751v2 [ quant-ph] 1 Jun 2023  \nVQE-generated Quantum Circuit Dataset for Machine Learning  \nAkimoto Nakayama, 1 Kosuke Mitarai, 1, 2, ∗ Leonardo Placidi, 1, 2 Takanori Sugimoto,2 and Keisuke Fujii 1, 2, 3,†  \n1 Graduate School of Engineering Science, Osaka University,  \n1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan  \n2 Center for Quantum Information and Quantum Biology,  \nOsaka University, 1-2 Machikaneyama, Toyonaka 560-0043, Japan  \n3 RIKEN Center for Quantum Computing (RQC), Hirosawa 2-1, Wako, Saitama 351-0198, Japan  \n(Dated: June 2, 2023)  \nQuantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical problems. While some artificial scenarios have shown that certain quantum machine learning techniques may be advantageous compared to their classical counterpart, it is unlikely that quantum machine learning will outclass traditional methods on popular classical datasets such as MNIST. In contrast, dealing with quantum data, such as quantum states or circuits, may be the task where we can benefit from quantum methods. Therefore, it is important to develop practically meaningful quantum datasets for which we expect quantum methods to be superior. In this paper, we propose a machine learning task that is likely to soon arise in the real world: clustering and classification of quantum circuits. We provide a dataset of quantum circuits optimized by the variational quantum eigensolver. We utilized six common types of Hamiltonians in condensed matter physics, with a range of 4 to 20 qubits, and applied ten different ans¨atze with varying depths (ranging from 3 to 32) to generatea quantum circuit dataset of six distinct classes, each containing 300 samples. We show that this dataset can be easily learned using quantum methods. In particular, we demonstrate a successful classification of our dataset using real 4-qubit devices available through IBMQ. By providing a setting and an elementary dataset where quantum machine learning is expected to be beneficial, we hope to encourage and ease the advancement of the field.  \nI. INTRODUCTION  \nQuantum machine learning has attracted much attention in recent years as a promising application of quantum computers [1, 2] . Many techniques, such as quantum neural networks [3–5], quantum generative models [6, 7], quantum kernel methods [8], and so on have been developed for achieving possible quantum speedups in machine learning tasks. They have also been realized experimentally [7–11] . While some artificial, carefully-designed scenarios have demonstrated that certain quantum machine learning techniques may be advantageous compared to classical methods [12–16], it is not yet clear whether quantum techniques would be beneficial for practical applications.  \nIn traditional machine learning, standard datasets, such as MNIST handwritten digits [17], are used to evaluate the performance and thus the practicality of new models. However, it is rather unlikely that quantum machine learning methods can outperform the state-ofthe-art classical machine learning procedures on those datasets, looking at their recent great success. With a large-scale numerical experiment involving up to 30 qubits, Huang et al. [13] have shown that the FashionMNIST dataset [18] is better learned by classical models. In contrast, when working with “quantum data”, such as quantum states or circuits, there is a good reason to be-  \n∗ [mitarai.kosuke.es@osaka-u.ac.jp](mitarai.kosuke.es@osaka-u.ac.jp)[ ](mitarai.kosuke.es@osaka-u.ac.jp)† [fujii@qc.ee.es.osaka-u.ac.jp](fujii@qc.ee.es.osaka-u.ac.jp)  \nlieve that quantum computers may provide a significant advantage. In another work by Huang et al. [19], it has been rigorously shown that quantum machine learning is beneficial when learning unknown quantum states or processes provided from physical experiments. It is therefore important to develop a prac","cbCaivbTs0fFQLpJ","https://ap.wps.com/l/cbCaivbTs0fFQLpJ","pdf",7680139,1,9,"English","en",105,"# Introduction\n## Motivation for quantum ML with quantum data\n## Related work on quantum datasets\n## Proposed task and dataset overview","[{\"question\":\"Why is a quantum dataset important for quantum machine learning?\",\"answer\":\"The paper argues that while classical ML dominates on popular classical datasets, quantum states or circuits are scenarios where quantum methods may provide a meaningful advantage. Therefore, practically useful quantum datasets are needed.\"},{\"question\":\"How is the dataset generated in this work?\",\"answer\":\"The dataset uses variational quantum eigensolver optimization to generate quantum circuits. It employs six Hamiltonian types from condensed matter physics, spans 4 to 20 qubits, and applies ten ansätze with depths ranging from 3 to 32.\"},{\"question\":\"What results demonstrate the dataset is learnable and where is it tested?\",\"answer\":\"The authors show the dataset can be learned using quantum methods and demonstrate successful classification using real 4-qubit devices available through IBMQ.\"}]","VQE-generated Quantum Circuit Dataset for Machine Learning | PDF",1785672146,23,{"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},"vqe-generated-quantum-circuit-dataset-for-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/vqe-generated-quantum-circuit-dataset-for-machine-learning/116867/",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},"Why is a quantum dataset important for quantum machine learning?","Question",{"text":75,"@type":76},"The paper argues that while classical ML dominates on popular classical datasets, quantum states or circuits are scenarios where quantum methods may provide a meaningful advantage. Therefore, practically useful quantum datasets are needed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset generated in this work?",{"text":80,"@type":76},"The dataset uses variational quantum eigensolver optimization to generate quantum circuits. It employs six Hamiltonian types from condensed matter physics, spans 4 to 20 qubits, and applies ten ansätze with depths ranging from 3 to 32.",{"name":82,"@type":73,"acceptedAnswer":83},"What results demonstrate the dataset is learnable and where is it tested?",{"text":84,"@type":76},"The authors show the dataset can be learned using quantum methods and demonstrate successful classification using real 4-qubit devices available through IBMQ.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]