[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116915-en":3,"doc-seo-116915-105":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},116915,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Quantum-Inspired Machine Learning - a Survey","Quantum-Inspired Machine Learning (QiML) is a fast-growing research area aiming to use principles of quantum mechanics to design classical machine learning algorithms within classical computing frameworks. Existing reviews often treat QiML only superficially and focus more broadly on Quantum Machine Learning (QML). This survey provides an integrated examination of QiML domains such as tensor network simulations and dequantized algorithms, summarizes recent advances and practical applications, clarifies term definitions, and outlines promising future research directions for both researchers and practitioners.","arXiv :2308 . 11269v1 [ cs .LG] 22 Aug 2023  \nQuantum-Inspired Machine Learning: a Survey  \nLarry Huynh, Jin Hong, Ajmal Mian, Hajime Suzuki, Yanqiu Wu, and Seyit Camtepe  \nAbstract—Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML’s diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues. Further, a concrete definition of QiML is established by analyzing various prior interpretations of the term and their inherent ambiguities. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning, enriching the field further. This survey serves as a guide for researchers and practitioners alike, providing a holistic understanding of QiML’s current landscape and future directions.  \nIndex Terms—Quantum-inspired Machine Learning, Quantum Machine Learning, Quantum Computing, Quantum Algorithms,  \nMachine Learning, Tensor Networks, Dequantized Algorithms, Quantum Circuit Simulation  \n~~ ~~ ✦ ~~ ~~  \n1 INTRODUCTION  \nTHE field of Quantum-Inspired Machine Learning  \n(QiML) has seen substantial growth, garnering interest from researchers globally. A specialized subset of Quantum Machine Learning (QML), QiML focuses on developing classical machine learning algorithms inspired by principles of quantum mechanics within a classical computational framework, commonly referenced as the “classical-classical”quadrant of QML categorization as shown in Figure 1. QiML represents a multifaceted research domain, with investigations pushing to exceed conventional, classical state-ofthe-art results, or exploring the expressivity provided by quantum formulations.  \nTo situate QiML within the context of QML, we briefly expound upon the latter. QML, more broadly, sits at the fascinating intersection of quantum computing and machine learning. The dominant research field concerns the“classical-quantum” domain, and explores the use of quantum hardware to accelerate and enhance machine learning strategies. Here, two challenges present in classical machine learning are addressed. First, the increasing size and complexity of datasets in many fields have created computational challenges that classical machine learning struggles to manage efficiently. Secondly, quantum computing offers the potential to solve complex problems that are currently infeasible with classical computation methods [1] . Practical evaluation of QML algorithms on actual quantum hardware, however, is currently limited by factors such as the limited number of qubits, high error rates in quantum gates, difficulty in maintaining quantum states (decoherence), and challenges associated with quantum error correction [2] . Asa result, the QML landscape has been primarily shaped by theoretical considerations, with recent advancements in noisy-intermediate scale quantum (NISQ) devices providing an early, empirical glimpse into the potential of full-scale  \nL. Huynh, J. Hong, and A. Mian are with the University of Western Australia. Emails: {larry.huynh, jin.hong, [ajmal.mian](ajmal.mian}@uwa.edu.au)[}](ajmal.mian}@uwa.edu.au)[@uwa.edu.au](ajmal.mian}@uwa.edu.au).  \nH. Suzuki, Y. Wu, and S. Camtepe are with CSIRO’s Data61, Marsfield, NSW, Australia. Emails: {[hajime.suzuki](hajime.suzuki), yanqiu.wu, [seyit.camtepe](seyit.camtepe}@data61.csiro.au)[}](seyit.camtepe}@data61.csiro.au)[@data61.csiro.au](seyit.camtepe}@data61.csiro.au).  \nFig. 1. ","cbCaidQVru1nl4Fk","https://ap.wps.com/l/cbCaidQVru1nl4Fk","pdf",2069058,1,56,"English","en",105,"# Introduction\n## Quantum-Inspired Machine Learning (QiML) within Quantum Machine Learning (QML)\n## Motivation and challenges in QML\n## QiML research domains and the literature gap\n## Survey aims and contributions","[{\"question\":\"What is Quantum-Inspired Machine Learning (QiML)?\",\"answer\":\"QiML develops classical machine learning algorithms inspired by principles of quantum mechanics, implemented within a classical computational framework.\"},{\"question\":\"Why does the survey focus on QiML rather than broader QML?\",\"answer\":\"Current review literature often explores QiML superficially and emphasizes QML overall, leaving a need for a standalone in-depth review focused on QiML as a distinct field.\"},{\"question\":\"Which QiML topics does the survey cover?\",\"answer\":\"The survey examines multiple research domains, including tensor network simulations and dequantized algorithms, and highlights recent advancements, practical applications, and future research directions.\"}]","Quantum-Inspired Machine Learning - 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