[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117816-en":3,"doc-seo-117816-105":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},117816,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Towards quantum enhanced adversarial robustness in machine learning","Machine learning methods are highly capable for data-driven tasks like image classification, yet they remain vulnerable to adversarial examples—inputs subtly manipulated to mislead the model. Linking machine learning with quantum computing offers the possibility of improved accuracy and efficiency while delivering stronger resistance to adversarial attacks. Recent advances using quantum phenomena have accelerated quantum adversarial machine learning, but deploying robust real-world tools still faces significant obstacles. This review summarizes progress, highlights key challenges, and outlines future research directions as quantum hardware scales and noise decreases.","Towards quantum enhanced adversarial robustness in machine learning  \nMaxwell T. West, 1, 􀀃 Shu-Lok Tsang,2 Jia S. Low,2 Charles D. Hill, 1, 3 Christopher Leckie,2 Lloyd C.L. Hollenberg, 1, 4 Sarah M. Erfani,2 and Muhammad Usman 1, 5, y  \n1 School of Physics, The University of Melbourne, Parkville, 3010, VIC, Australia  \n2 School of Computing and Information Systems, The University of Melbourne, Parkville, 3010, VIC, Australia  \n3 School of Mathematics and Statistics, The University of Melbourne, Parkville, 3010, VIC, Australia  \n4 Center for Quantum Computation and Communication Technologies,  \nThe University of Melbourne, Parkville, 3010, VIC, Australia  \n5 Data61, CSIRO, Clayton, 3168, VIC, Australia  \narXiv :2306 . 12688v1 [ quant-ph] 22 Jun 2023  \nMachine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm-remains a serious challenge. The integration of machine learning with quantum computing has the potential to yield tools offering not only better accuracy and computational efficiency, but also superior robustness against adversarial attacks. Indeed, recent work has employed quantum mechanical phenomena to defend against adversarial attacks, spurring the rapid development of the field of quantum adversarial machine learning (QAML) and potentially yielding a new source of quantum advantage. Despite promising early results, there remain challenges towards building robust real-world QAML tools. In this review we discuss recent progress in QAML and identify key challenges. We also suggest future research directions which could determine the route to practicality for QAML approaches as quantum computing hardware scales up and noise levels are reduced.  \nMachine learning (ML) algorithms are ubiquitous nowadays and underpin the vast majority of autonomous and robotic systems, including those deployed in security applications such as facial recognition, data classification, surveillance, and security systems for military applications [1] . In these settings, the robustness of ML algorithms is of critical importance, with any vulnerability to data manipulation potentially posing a serious security threat. This has instigated a major new subfield within machine learning, namely adversarial machine learning. Adversarial ML is concerned with the process of generating inputs which will be misclassified by a targeted ML system (typically a neural network), despite being only perturbed by a small amount from an initial, correctly classified input [2–5] (see Figure 1) . While modern neural networks are generally resilient to minor random perturbations of their inputs, they can be extremely susceptible to nonrandom, carefully crafted ones as shown in Figure 1(b) . In the case of high resolution image classification, even state-of-the-art convolutional neural networks can be fooled by adding to a clean image perturbations which are so small they are completely imperceptible to human eyes [3], or possibly consisting of a change to only a single pixel [6] . The surprising brittleness of such powerful classifiers has been intensively studied in recent years, with increasingly sophisticated methods of attacking [7–11] (i. e. generating adversarial examples) and defending [12–15] neural networks developed.  \nIn a world where security sensitive tasks are beginning to be outsourced to ML frameworks, it is imperative to fully understand the nature of the mechanism by which neural networks may be tricked by seemingly innocuous examples which are all but indistinguishable from genuine  \n􀀃 [westm2@student.unimelb.edu.au](westm2@student.unimelb.edu.au)[ ](westm2@student.unimelb.edu.au)[y](y musman@unimelb.edu.au)[ musman@unimelb.edu.au](y musman@unimelb.edu.au)  \ndata [16] . This need is heightened by the recent discoveries of adversarial attacks originating not from applying such perturbations digit","cbCaibAIknzSJ1cx","https://ap.wps.com/l/cbCaibAIknzSJ1cx","pdf",4678070,1,11,"English","en",105,"# Introduction\n## Adversarial examples and adversarial machine learning\n## Quantum machine learning and QAML\n## Challenges toward practical robustness\n# Quantum-enhanced robustness directions\n## Variational quantum circuit frameworks\n## Concentration of measure and vulnerability\n## Physical-world adversarial threats","[{\"question\":\"What problem does the document focus on in machine learning?\",\"answer\":\"It focuses on the vulnerability of machine learning models to adversarial examples—inputs perturbed in a way that can cause misclassification.\"},{\"question\":\"How does quantum computing relate to adversarial robustness?\",\"answer\":\"Integrating quantum computing with machine learning may produce tools with enhanced adversarial robustness, potentially offering improved accuracy, efficiency, and resistance to adversarial attacks.\"},{\"question\":\"What are the main challenges for real-world quantum adversarial machine learning (QAML)?\",\"answer\":\"Despite promising early results, the document notes remaining challenges in building robust real-world QAML tools, motivating future research as hardware scales and noise decreases.\"}]","Towards quantum enhanced adversarial robustness in machine learning | 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problem does the document focus on in machine learning?","Question",{"text":76,"@type":77},"It focuses on the vulnerability of machine learning models to adversarial examples—inputs perturbed in a way that can cause misclassification.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does quantum computing relate to adversarial robustness?",{"text":81,"@type":77},"Integrating quantum computing with machine learning may produce tools with enhanced adversarial robustness, potentially offering improved accuracy, efficiency, and resistance to adversarial attacks.",{"name":83,"@type":74,"acceptedAnswer":84},"What are the main challenges for real-world quantum adversarial machine learning (QAML)?",{"text":85,"@type":77},"Despite promising early results, the document notes remaining challenges in building robust real-world QAML tools, motivating future research as hardware scales and noise 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