[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125603-en":3,"doc-seo-125603-105":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},125603,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","TrojanNet - Detecting Trojans in Quantum Circuits using Machine Learning","Quantum computing offers major potential, while its security remains a central challenge. Quantum circuits depend on high-quality compilers to reduce depth and gate count on today’s noisy hardware, yet untrusted and efficient third-party compilers can introduce tampering via Trojan gate insertion. TrojanNet detects and classifies Trojan-inserted QAOA circuits using a convolutional neural network. For training, Qiskit-generated datasets vary Trojan gate types, counts, insertion locations, and compiler backends across original and corresponding infected circuits. Results report an average accuracy of 98.80% and an average F1-score of 98.53%, followed by comparison to existing Trojan-detection approaches.","TrojanNet: Detecting Trojans in Quantum Circuits  \nusing Machine Learning  \nSubrata Das  \nDept. of Electrical Engineering Pennsylvania State University University Park, PA[sjd6366@psu.edu](sjd6366@psu.edu)  \nSwaroop Ghosh  \nDept. of Electrical Engineering Pennsylvania State University University Park, PA[szg212@psu.edu](szg212@psu.edu)  \narXiv :2306 . 16701v1 [ quant-ph] 29 Jun 2023  \nAbstract—Quantum computing holds tremendous potential for various applications, but its security remains a crucial concern. Quantum circuits need high-quality compilers to optimize the depth and gate count to boost the success probability on current noisy quantum computers. There is a rise of efficient but unreliable/untrusted compilers; however, they present a risk of tampering such as Trojan insertion. We propose TrojanNet, a novel approach to enhance the security of quantum circuits by detecting and classifying Trojan-inserted circuits. In particular, we focus on the Quantum Approximate Optimization Algorithm (QAOA) circuit that is popular in solving a wide range of optimization problems. We investigate the impact of Trojan insertion on QAOA circuits and develop a Convolutional Neural Network (CNN) model, referred to as TrojanNet, to identify their presence accurately. Using the Qiskit framework, we generate 12 diverse datasets by introducing variations in Trojan gate types, the number of gates, insertion locations, and compiler backends. These datasets consist of both original Trojan-free QAOA circuits and their corresponding Trojan-inserted counterparts. The generated datasets are then utilized for training and evaluating the TrojanNet model. Experimental results showcase an average accuracy of 98.80% and an average F1-score of 98.53% in effectively detecting and classifying Trojan-inserted QAOA circuits. Finally, we conduct a performance comparison between TrojanNet and existing machine learning-based Trojan detection methods specifically designed for conventional netlists. Index Terms—Quantum security, Hardware Trojan, QAOA  \nI. INTRODUCTION  \nQuantum computing has emerged as a groundbreaking technology with the potential to revolutionize various fields such as optimization, cryptography, and material science [1] . Quantum Approximate Optimization Algorithm (QAOA) circuits play a crucial role in solving a wide range of combinatorial optimization problems using quantum computers [2], [3] . These circuits enable the exploration of the optimization landscape using variational techniques to find nearoptimal solutions, making them invaluable tools for addressing complex optimization tasks. Their versatility and scalability have garnered significant interest, positioning QAOA circuits as a fundamental component of quantum computing research and applications. These circuits often encapsulate proprietary algorithms, novel optimization strategies, or domain-specific knowledge. For instance, a QAOA circuit designed for optimizing financial portfolio management may encapsulate an  \nintricate combination of portfolio risk assessment and asset allocation techniques.  \nIn order to fully leverage the promising advantages of quantum computing, it is of utmost importance to prioritize its security and privacy [4], [5] . Quantum circuits, especially those utilizing QAOA, heavily rely on high-quality compilers to optimize their performance [6] . These compilers play a crucial role in reducing the depth and gate count of quantum circuits, which in turn increases the likelihood of success on quantum computers that are susceptible to noise and errors. However, recent developments have revealed the emergence of numerous efficient third-party compilers that claim to offer superior optimization capabilities for complex quantum circuits compared to their well-established counterparts [7],[8] . While these compilers may seem attractive in terms of efficiency, their reliability is uncertain. One of the main risks associated with relying on unreliable compilers is the potentia","cbCaiiT1nj6RDOXK","https://ap.wps.com/l/cbCaiiT1nj6RDOXK","pdf",3050021,1,9,"English","en",105,"# Abstract\n# Introduction\n## Quantum computing security and compilers\n## QAOA role in optimization\n## Risk of Trojan insertion by untrusted compilers\n## Proposed idea: TrojanNet with CNNs\n## Contributions","[{\"question\":\"What security risk does the paper focus on in quantum circuit compilation?\",\"answer\":\"It focuses on tampering caused by inserting Trojan gates through untrusted or unreliable third-party compilers.\"},{\"question\":\"How does TrojanNet detect Trojans in QAOA circuits?\",\"answer\":\"TrojanNet uses a convolutional neural network to identify subtle structural patterns in compiled QAOA circuits, including disruptions to the expected layer regularity.\"},{\"question\":\"What datasets and variations are used to train and evaluate the model?\",\"answer\":\"The work generates 12 datasets in Qiskit by varying Trojan gate types, the number of gates, insertion locations, and compiler backends, including both Trojan-free and Trojan-inserted circuit pairs.\"}]","TrojanNet - Detecting Trojans in Quantum Circuits using Machine Learning | PDF",1785900174,23,{"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},"trojannet-detecting-trojans-in-quantum-circuits-using-machine-learning","",{"@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/trojannet-detecting-trojans-in-quantum-circuits-using-machine-learning/125603/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What security risk does the paper focus on in quantum circuit compilation?","Question",{"text":75,"@type":76},"It focuses on tampering caused by inserting Trojan gates through untrusted or unreliable third-party compilers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does TrojanNet detect Trojans in QAOA circuits?",{"text":80,"@type":76},"TrojanNet uses a convolutional neural network to identify subtle structural patterns in compiled QAOA circuits, including disruptions to the expected layer regularity.",{"name":82,"@type":73,"acceptedAnswer":83},"What datasets and variations are used to train and evaluate the model?",{"text":84,"@type":76},"The work generates 12 datasets in Qiskit by varying Trojan gate types, the number of gates, insertion locations, and compiler backends, including both Trojan-free and Trojan-inserted circuit pairs.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]