[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117981-en":3,"doc-seo-117981-105":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},117981,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","sQUlearn - A Python Library for Quantum Machine Learning","sQUlearn presents a user-friendly, NISQ-ready Python library for quantum machine learning, built to integrate seamlessly with classical machine learning workflows such as scikit-learn. Its dual-layer architecture supports both researchers and practitioners, streamlining efficient prototyping, experimentation, and pipeline construction. The library covers quantum kernel methods and quantum neural networks, offering customizable data encoding, automated execution handling, and specialized kernel regularization. With NISQ compatibility and end-to-end automation, it enables flexible switching across Qiskit and PennyLane and between simulation and real hardware execution.","arXiv :2311 .08990v2 [ quant-ph] 19 Apr 2024  \nsQUlearn – A Python Library for Quantum Machine Learning  \nDavid A. Kreplin, ∗ Moritz Willmann,† Jan Schnabel,† Frederic Rapp,† Manuel Hagelüken,† and Marco Roth‡ Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Nobelstraße 12, D-70569 Stuttgart, Germany  \n(Dated: October 28, 2024)  \nsQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library’s dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive toolset that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By focusing on NISQ-compatibility and end-to-end automation, sQUlearn aims to bridge the gap between current quantum computing capabilities and practical machine learning applications. The library provides substantial flexibility, enabling quick transitions between the underlying quantum frameworks Qiskit and PennyLane, as well as between simulation and running on actual hardware.  \nI. INTRODUCTION  \nMachine Learning (ML) is a remarkably successful discipline that has been rapidly adopted broadly in science, industry and society, and is widely believed to have the potential to completely transform a wide range of industries in the upcoming years [1] . While its recent ascent to prominence is notable, the roots of ML extend back to the early 1960s [2], reflecting a path of varied progress and challenges. Besides some initial set-backs [3] the foundations of today’s deep neural networks that drive a lot of the recent advancements have already been established almost half a century ago [4, 5] . The reasons for the accelerated breakthroughs of the past decade are essentially threefold: (a) an increased computational resources,(b) the availability of large-scale data, and (c) the emergence of development tools that abstract away low-level complexity. While these factors have enabled remarkable breakthroughs, the evolving landscape of ML presents new frontiers, one of which is quantum machine learning (QML) .  \nQuantum machine learning has emerged as an innovative approach that explores different capabilities and potentials within the field, leveraging the principles of quantum mechanics to enhance computational power and efficiency [6] . Some techniques seek to accelerate performance by executing quantum variants of linear algebra procedures [7–10], effectively using the quantum computer as an hardware accelerator, similar to a GPU. However, these methods usually involve deep quantum circuits with many gates. The resulting complexity often exceeds the capabilities of current noisy intermediatescale quantum (NISQ) hardware [11] . This has spawned an increased interest in NISQ-compatible models which are often not just quantum-enhanced versions of classical algorithms but rather models that have an intrinsic quantum nature. Quantum kernel methods, for example, have been shown to have the potential to outperform their classical counterparts in specific tasks, making QML an attractive domain for further research and practical applications [12, 13] .  \n∗ [david.kreplin@ipa.fraunhofer.de](david.kreplin@ipa.fraunhofer.de)[ ](david.kreplin@ipa.fraunhofer.de)† These authors contributed equally ‡ [marco.roth@ipa.fraunhofer.de](marco.roth@ipa.fraunhofer.de)  \nDespite these successes, the situation of QML today is comparable to that of classical ML a few decades ago. Translating the success factors (a)–(c) of classical ML to QML, we observe that the progress of the hardware developments poses a major bottleneck for the adaptability of QML algorithms for practical usage [cf. (a)] . In terms of data [cf. (b)], we","cbCaioHYiDQ3pxig","https://ap.wps.com/l/cbCaioHYiDQ3pxig","pdf",751067,1,15,"English","en",105,"# Introduction\n## Motivation from classical machine learning to QML\n## NISQ constraints and quantum kernel advantages\n## sQUlearn goals and compatibility with scikit-learn\n## High-level QML methods and available implementations","[{\"question\":\"What is sQUlearn designed for?\",\"answer\":\"sQUlearn is a NISQ-ready Python library for quantum machine learning that aims to integrate smoothly with classical ML tools such as scikit-learn.\"},{\"question\":\"Which QML approaches does sQUlearn support?\",\"answer\":\"It provides implementations for both quantum kernel methods and quantum neural networks, including variants for classification and regression like QSVM and quantum Gaussian processes.\"},{\"question\":\"How does sQUlearn improve practical usability?\",\"answer\":\"It focuses on NISQ-compatibility and end-to-end automation, offering features such as customizable data encoding, automated execution handling, and flexible integration with Qiskit and PennyLane, including switching between simulation and real hardware.\"}]","sQUlearn - 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