[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122566-en":3,"doc-seo-122566-105":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},122566,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","QUANTUM ADVERSARIAL MACHINE LEARNING AND DEFENSE STRATEGIES - Challenges and Opportunities","As quantum computing advances, building quantum-secure neural networks becomes essential to mitigate adversarial threats and preserve model integrity. The paper presents three quantum-secure design principles: leveraging post-quantum cryptography, adopting quantum-resistant neural network architectures, and enforcing transparent and accountable development and deployment. It supports these principles with approaches such as quantum data anonymization, quantum-resistant neural networks, and quantum encryption. The work also highlights open problems in quantum security, privacy, and trust, proposing adaptive and auto adversarial attacks as future research directions.","QUANTUM ADVERSARIAL MACHINE LEARNING AND DEFENSE STRATEGIES: CHALLENGES AND OPPORTUNITIES  \nA PREPRINT  \narXiv :2412 . 12373v1 [ quant-ph] 16 Dec 2024  \nEric Yocam  \nThe Beacom College of Computer and Cyber Sciences Dakota State University Madison, SD 57042 [eric.yocam@trojans.dsu.edu](eric.yocam@trojans.dsu.edu)  \nMahesh Kamepalli  \nThe Beacom College of Computer and Cyber Sciences Dakota State University Madison, SD 57042 [mahesh.kamepalli@trojans.dsu.edu](mahesh.kamepalli@trojans.dsu.edu)  \nAnthony Rizi  \nThe Beacom College of Computer and Cyber Sciences Dakota State University Madison, SD 57042 [anthony.rizi@trojans.dsu.edu](anthony.rizi@trojans.dsu.edu)  \nVarghese Vaidyan  \nThe Beacom College of Computer and Cyber Sciences Dakota State University Madison, SD 57042 [varghese.vaidyan@dsu.edu](varghese.vaidyan@dsu.edu)  \nYong Wang  \nThe Beacom College of Computer and Cyber Sciences Dakota State University Madison, SD 57042 [yong.wang@dsu.edu](yong.wang@dsu.edu)  \nGurcan Comert  \nEngineering and Computer Science Department Benedict College Columbia, SC 29204 [Gurcan.Comert@Benedict.edu](Gurcan.Comert@Benedict.edu)  \nDecember 18, 2024  \nABSTRACT  \nAs quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles:  \n(1) using post-quantum cryptography,(2) employing quantum-resistant neural network architectures, and (3) ensuring transparent and accountable development and deployment. These principles are supported by various quantum strategies, including quantum data anonymization, quantum-resistant neural networks, and quantum encryption. The paper also identifies open issues in quantum security, privacy, and trust, and recommends exploring adaptive adversarial attacks and auto adversarial attacks as future directions. The proposed design principles and recommendations provide guidance for developing quantum-secure neural networks, ensuring the integrity and reliability of machine learning models in the quantum era.  \nKeywords Quantum-secure neural networks · Post-quantum cryptography · Quantum-resistant neural networks · Transparent and accountable development · Adversarial attacks  \n1 Introduction  \nA defense against quantum-enabled adversarial attacks necessitates rethinking conventional machine learning security, privacy, and trust. Quantum computing fundamentally alters the classical computing threat landscape and expands theset of possible adversarial attacks. New quantum computing defense strategies will be required to protect quantum machine learning models against an evolved threat landscape Miller et al. (2020) .  \n1.1 Quantum-evolved threats  \nFirst, rapid advances in quantum computing hardware and algorithms. Second, several major technology companies and research labs now have functioning quantum processors with 10-100 qubits. Third, quantum systems are nearing a point for nascent quantum machine learning applications. Fourth, the field of adversarial machine learning has demonstrated that classical machine learning models have inherent vulnerabilities. Finally, quantum computers are poised to take adversarial attacks to the next level by running more sophisticated optimization algorithms to find adversarial examples.  \n1.2 Quantum emergence  \nQuantum computing is growing in relevance as the technology progresses. This technological progress is underpinned by a surge in investments from both public and private sectors, driving substantial progress in quantum hardware and algorithms. In Feder (2020), it was observed that IonQ made an 11-qubit quantum computer accessible to the public through the Amazon Braket cloud platform, and on October 1st, they revealed a 32-qubit iteration. This heightened financial backing is propelling innovation and accelerating the development of quantum technologies worldwide.  \n1.3 Quantum-driven interest  \nThe expanding community of researchers, scientists, a","cbCairhnrOuneQTY","https://ap.wps.com/l/cbCairhnrOuneQTY","pdf",752569,1,24,"English","en",105,"# Introduction\n## Quantum-evolved threats\n## Quantum emergence\n## Quantum-driven interest\n## Quantum adversarial threats","[{\"question\":\"Why are quantum-secure neural networks necessary as quantum computing progresses?\",\"answer\":\"Quantum computing changes the threat landscape and increases the range and sophistication of possible adversarial attacks. Quantum-secure neural networks help protect machine learning models against this evolved risk environment.\"},{\"question\":\"What three quantum-secure design principles does the paper propose?\",\"answer\":\"The paper proposes (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring transparent and accountable development and deployment.\"},{\"question\":\"How does the paper envision future work on adversarial attacks in the quantum context?\",\"answer\":\"It recommends exploring adaptive adversarial attacks and auto adversarial attacks as future directions, alongside identifying open issues in quantum security, privacy, and trust.\"}]","QUANTUM ADVERSARIAL MACHINE LEARNING AND DEFENSE STRATEGIES - Challenges and Opportunities | PDF",1785811352,60,{"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},"quantum-adversarial-machine-learning-and-defense-strategies-challenges-and-opportunities","",{"@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/quantum-adversarial-machine-learning-and-defense-strategies-challenges-and-opportunities/122566/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are quantum-secure neural networks necessary as quantum computing progresses?","Question",{"text":75,"@type":76},"Quantum computing changes the threat landscape and increases the range and sophistication of possible adversarial attacks. Quantum-secure neural networks help protect machine learning models against this evolved risk environment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What three quantum-secure design principles does the paper propose?",{"text":80,"@type":76},"The paper proposes (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring transparent and accountable development and deployment.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper envision future work on adversarial attacks in the quantum context?",{"text":84,"@type":76},"It recommends exploring adaptive adversarial attacks and auto adversarial attacks as future directions, alongside identifying open issues in quantum security, privacy, and trust.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]