[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120137-en":3,"doc-seo-120137-105":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":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},120137,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predominant Aspects on Security for Quantum Machine Learning - Literature Review","Quantum Machine Learning (QML) bridges quantum computing and classical machine learning, promising advances in computational tasks while raising new security questions. This systematic literature review analyzes security concerns and strengths across QML models, including vulnerabilities tied to quantum architectures and the mitigations proposed by prior work. The survey highlights novel attack vectors absent in classical systems and identifies risks such as cross-talk in superconducting setups and repeated shuttle operations in ion-trap platforms. It also evaluates defenses, including adversarial training, quantum noise exploitation, and quantum differential privacy, and emphasizes continued rigorous research for secure real-world deployment.","Predominant Aspects on Security for Quantum Machine Learning: Literature Review  \nNicola Franco∗ , Alona Sakhnenko∗ , Leon Stolpmann†, Daniel Thuerck‡, Fabian Petsch§ , Annika Rüll§ , Jeanette Miriam Lorenz∗  \n∗ Fraunhofer Institute for Cognitive Systems (IKS), Munich, Germany †adesso Switzerland, Zurich, Switzerland  \n‡Quantagonia, Bad Homburg, Germany  \n§ Federal Office for Information Security (BSI), Bonn, Germany  \narXiv :2401 .07774v3 [ quant-ph] 28 Jul 2024  \nAbstract—Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review. We categorize and review the security of QML models, their vulnerabilities inherent to quantum architectures, and the mitigation strategies proposed. The survey reveals that while QML possesses unique strengths, it also introduces novel attack vectors not seen in classical systems. We point out specific risks, such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems, which threaten QML’s reliability. However, approaches like adversarial training, quantum noise exploitation, and quantum differential privacy have shown potential in enhancing QML robustness. Our review discuss the need for continued and rigorous research to ensure the secure deployment of QMLin real-world applications. This work serves as a foundational reference for researchers and practitioners aiming to navigate the security aspects of QML.  \nIndex Terms—Security, Quantum Machine Learning, Quantum Computing.  \nI. INTRODUCTION  \nIn recent years, there has been a marked escalation in quantum technology capabilities, both from the perspectives of engineering and algorithmic design. Many global players are joining in on this venture and are developing their own Quantum Computing (QC) stacks. Large-scale noise-resilient quantum computers have a proven potential to accelerate the computation of algorithms in many domains [1, 2], however, these types of computers will not be available in the near future. Consequently, much of the current research is centered on harnessing the capabilities of noisy intermediatescale quantum (NISQ) devices [3] . Machine Learning (ML) is widely believed to have potential to be one of the first applications to benefit from NISQ devices [4, 5, 6] . Pioneering efforts in Quantum ML (QML) have led to the development of methods such as quantum clustering [4], quantum deep learning [7], and quantum reinforcement learning [8] . However, the intersection of QC and ML also spawns unique security challenges, extending beyond conventional ML concerns [9] .  \nThis literature survey examines the security aspects of QML, focusing on both vulnerabilities and defenses. Thereview systematically studies vulnerabilities specific to QML,  \nsuch as security issues with quantum classifiers in higher dimensions and attack strategies targeting quantum data encodings or leveraging quantum noise. These findings are important due to the complexity of QML and its potential for practical use, highlighting the need for advanced verification methods to ensure security while achieving quantum advantage. Additionally, the survey discusses proactive defense methods. These methods include adversarial training, adding privacy measures for data protection, verifying model robustness, and considering hardware noise not only as a weakness but also as a benefit for improving QML model robustness.  \nBy providing a comprehensive analysis of both vulnerabilities and defenses, this survey equips researchers and practitioners with a deeper understanding of the challengesand opportunities in securing QML.  \nThe paper is organized as follows: Section II provides an overview of the basic principles of QC and QML. Section III presents the approach used f","cbCaifG0qwg4dreH","https://ap.wps.com/l/cbCaifG0qwg4dreH","pdf",416296,1,11,"English","en",105,"# Introduction\n## What Is QML?\n## Approach and Systematic Survey Method\n## Unique Challenges of QML Models\n## Defense Mechanisms and Resilience\n## Guidance for Practitioners and Future Work","[{\"question\":\"What does the literature review focus on regarding QML security?\",\"answer\":\"It studies both vulnerabilities and defenses in Quantum Machine Learning, including issues specific to QML models and proposed mitigation strategies.\"},{\"question\":\"Which types of QML-related risks are highlighted?\",\"answer\":\"The review points to risks such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems that can undermine reliability.\"},{\"question\":\"What defenses does the review discuss to improve QML robustness?\",\"answer\":\"It discusses approaches like adversarial training, quantum noise exploitation, and quantum differential privacy, along with treating hardware noise as a potential benefit for robustness.\"}]","Predominant Aspects on Security for Quantum Machine Learning - 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