[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119729-en":3,"doc-seo-119729-105":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},119729,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","MNISQ - A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ - Preprint","Introduces MNISQ, a large-scale dataset for both quantum and classical machine learning in the Noisy Intermediate-Scale Quantum era. The dataset contains 4,950,000 data points organized into 9 subdatasets and is provided in a dual format: quantum circuits and classical quantum-circuit descriptions using QASM. Performs circuit classification with quantum kernel methods and classical architectures (S4, Transformer, LSTM), reporting up to 97% accuracy with quantum approaches and up to 77% accuracy with S4 on tokenized QASM sequences.","arXiv :2306 . 16627v1 [ quant-ph] 29 Jun 2023  \nMNISQ:  \nA Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ  \nera  \nLeonardo Placidi  \nOsaka University-QIQB Osaka, Japan  \n[u770335b@ecs.osaka-u.ac.jp](u770335b@ecs.osaka-u.ac.jp)  \nRyuichiro Hataya  \nRIKEN Tokyo, Japan  \n[ryuichiro.hataya@riken.jp](ryuichiro.hataya@riken.jp)  \nToshio Mori  \nOsaka University-QIQB-RIKEN Osaka-Wako Saitama, Japan [t.mori.qiqb@osaka-u.ac.jp](t.mori.qiqb@osaka-u.ac.jp)  \nKoki Aoyama  \nOsaka University Osaka, Japan  \n[k-aoyama@ist.osaka-u.ac.jp](k-aoyama@ist.osaka-u.ac.jp)  \nHayata Morisaki  \nOsaka University Osaka, Japan  \n[u748119d@ecs.osaka-u.ac.jp](u748119d@ecs.osaka-u.ac.jp)  \nKosuke Mitarai  \nOsaka University-QIQB Osaka, Japan  \n[mitarai.kosuke.es@osaka-u.ac.jp](mitarai.kosuke.es@osaka-u.ac.jp)  \nKeisuke Fujii  \nOsaka University-QIQB-RIKEN Osaka-Wako Saitama, Japan [fujii.keisuke.es@osaka-u.ac.jp](fujii.keisuke.es@osaka-u.ac.jp)  \nAbstract  \nWe introduce the first large-scale dataset, MNISQ, for both the Quantum and the Classical Machine Learning community during the Noisy Intermediate-Scale Quantum era. MNISQ consists of 4,950,000 data points organized in 9 subdatasets. Building our dataset from the quantum encoding of classical information (e.g., MNIST dataset), we deliver a dataset in a dual form: in quantum form, as circuits, and in classical form, as quantum circuit descriptions (quantum programming language, QASM) . In fact, also the Machine Learning research related to quantum computers undertakes a dual challenge: enhancing machine learning exploiting the power of quantum computers, while also leveraging state-of-the-art classical machine learning methodologies to help the advancement of quantum computing. Therefore, we perform circuit classification on our dataset, tackling the task with both quantum and classical models. In the quantum endeavor, we test our circuit dataset with Quantum Kernel methods, and we show excellent results up to 97% accuracy. In the classical world, the underlying quantum mechanical structures within the quantum circuit data are not trivial. Nevertheless, we test our dataset on three classical models: Structured State Space sequence model (S4), Transformer and LSTM. In particular, the S4 model applied on the tokenized QASM sequences reaches an impressive 77% accuracy. These findings illustrate that quantum circuitrelated datasets are likely to be quantum advantageous, but also that state-of-the-art machine learning methodologies can competently classify and recognize quantum  \nPreprint. Under review.  \ncircuits. We finally entrust the quantum and classical machine learning community the fundamental challenge to build more quantum-classical datasets like ours and to build future benchmarks from our experiments. The dataset is accessible on GitHub and its circuits are easily run in qulacs or qiskit.  \n1 Introduction  \n1.1 Background  \nThe advent of quantum computers has garnered significant attention across various scientific fields. By harnessing the principles of quantum mechanics, these computers are capable of performing calculations that are beyond the capabilities of classical computers [1] . While classical computers operate using bits that can be either 0 or 1, quantum computers use qubits that can take a superposed state of 0 and 1. This quantum property enables calculations much faster than those on classical computers. As a result, quantum computers excel at solving specific problems, such as prime factorization [2], simulations of quantum many-body systems [3], and linear system solvers [4] . With the realization of quantum computers comprising 50 to 100 qubits, the field has entered the era of quantum computational supremacy [5–8] . In this era, even the most powerful supercomputers struggle to simulate the behavior of quantum computers. However, this argument is based on a benchmark task known as random quantum circuit sampling [5], and the usefulness of quantum ","cbCaimpXjzCex6SM","https://ap.wps.com/l/cbCaimpXjzCex6SM","pdf",2142256,1,23,"English","en",105,"# Abstract\n# Introduction\n## Background\n## Motivation","[{\"question\":\"What is MNISQ and who is it designed for?\",\"answer\":\"MNISQ is a large-scale dataset intended for both the quantum and the classical machine learning community during the NISQ era.\"},{\"question\":\"How is MNISQ structured and what formats does it provide?\",\"answer\":\"It includes 4,950,000 data points organized into 9 subdatasets, delivered both as quantum circuits and as classical QASM-based circuit descriptions.\"},{\"question\":\"How are models evaluated on the MNISQ circuit classification task?\",\"answer\":\"The work applies quantum kernel methods for quantum models and uses classical models including S4, Transformer, and LSTM for classical benchmarks.\"}]","MNISQ - A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ - Preprint | PDF",1785725989,58,{"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},"mnisq-a-large-scale-quantum-circuit-dataset-for-machine-learning-onfor-quantum-computers-in-the-nisq-preprint","",{"@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/mnisq-a-large-scale-quantum-circuit-dataset-for-machine-learning-onfor-quantum-computers-in-the-nisq-preprint/119729/",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","2026-08-03",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 MNISQ and who is it designed for?","Question",{"text":75,"@type":76},"MNISQ is a large-scale dataset intended for both the quantum and the classical machine learning community during the NISQ era.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is MNISQ structured and what formats does it provide?",{"text":80,"@type":76},"It includes 4,950,000 data points organized into 9 subdatasets, delivered both as quantum circuits and as classical QASM-based circuit descriptions.",{"name":82,"@type":73,"acceptedAnswer":83},"How are models evaluated on the MNISQ circuit classification task?",{"text":84,"@type":76},"The work applies quantum kernel methods for quantum models and uses classical models including S4, Transformer, and LSTM for classical benchmarks.","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"]