[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123399-en":3,"doc-seo-123399-105":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},123399,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",6,"Technology","ANOMALY DETECTION AGENT EMPOWERED BY QUANTUM AI AND MACHINE LEARNING","The document discloses a quantum-AI–enabled anomaly detection agent designed for high-volume log data in IT and cybersecurity. It addresses limitations of rule-based and conventional machine-learning approaches used in Splunk-like environments, including poor scalability, high false positives, costly manual feature engineering, and inability to surface novel or emergent threats hidden in noisy logs. The disclosure proposes a scalable, adaptive, and explainable solution, supported by described architecture, processing flow, and an implementation-oriented computer system.","Technical Disclosure Commons  \nDefensive Publications Series  \n02 Sep 2025  \nANOMALY DETECTION AGENT EMPOWERED BY QUANTUM AI AND MACHINE LEARNING  \nFuming Guo VISA  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nGuo, Fuming, \"ANOMALY DETECTION AGENT EMPOWERED BY QUANTUM AI AND MACHINE LEARNING\", Technical Disclosure Commons,(September 02, 2025)  \n[https://www.tdcommons.org/dpubs_series/8540](https://www.tdcommons.org/dpubs_series/8540)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nANOMALY DETECTION AGENT EMPOWERED BY QUANTUM AI AND  \nMACHINE LEARNING  \nVISA  \nINVENTOR:  \nFuming Guo  \nPublished by Technical Disclosure Commons, 2025 2  \nTECHNICAL FIELD  \n[0001] The present disclosure generally relates to the technical field of Quantum Artificial Intelligence (AI), and specifically relates to advanced anomaly detection techniques for high  \nvolume log data in Information Technology (IT) and cybersecurity applications.  \nBACKGROUND  \n[0002] The current state of technology in the field of anomaly detection within the context of Splunk deployments, for high-volume and complex log data environments, is characterized by substantial challenges. Traditional anomaly detection methods, primarily rule-based systems, struggle to effectively process and analyze the large and complex data sets generated by modern IT systems, applications, and networks. The conventional techniques often encounter difficulties due to the high dimensionality and variability of log data formats, leading to inefficiencies in feature engineering and the ability to discern subtle anomalies amidst significant data noise. While standard machine learning approaches have been introduced as alternatives, they frequently yield high false positive rates and necessitate extensive manual rule creation and tuning, which is both time-consuming and often inadequate in dynamically evolving threat landscapes. Despite advances in machine learning, existing anomaly detection solutions still fall short in scalability and adaptability, rendering organizations vulnerable to security threats and operational inefficiencies. The limitations of these systems are further highlighted by their inability to identify novel threats or emergent patterns, which remain  \nhidden within the vast array of log data.  \n[0003] Accordingly, there exists a need in the art for a scalable, adaptive, and explainable anomaly detection solution that efficiently handles the complexities of contemporary log data  \nenvironments.  \n[0004] The information disclosed in the background section of the disclosure is only for the enhancement of understanding of the general background of the invention and should not betaken as an acknowledgement or any form of suggestion that this information forms the prior  \nart already known to a person skilled in the art.  \nBRIEF DESCRIPTION OF THE DRAWINGS  \n[0005] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the  \n[https://www.tdcommons.org/dpubs_series/8540](https://www.tdcommons.org/dpubs_series/8540) 3  \nfigure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of device or system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:  \n[0006] FIG. 1 illustrates an exemplary architecture for an ","cbCaive7vDm1lo84","https://ap.wps.com/l/cbCaive7vDm1lo84","pdf",353869,1,16,"English","en",105,"# Technical Field\n# Background\n# Brief Description of the Drawings\n# Description of the Disclosure","[{\"question\":\"What problem does the anomaly detection agent target?\",\"answer\":\"It targets scalable and explainable detection of anomalies in high-volume, high-dimensional, and variable log data for IT and cybersecurity use cases.\"},{\"question\":\"Why are traditional anomaly detection methods insufficient in Splunk deployments?\",\"answer\":\"Rule-based techniques struggle with large complex datasets and log variability, while conventional ML often produces high false positives and requires extensive manual rule creation and tuning.\"},{\"question\":\"What evidence does the disclosure provide to support the proposed system?\",\"answer\":\"It describes exemplary drawings, including a dual-agent architecture, an anomaly detection flowchart, and a computer system block diagram for implementing the embodiments.\"}]","ANOMALY DETECTION AGENT EMPOWERED BY QUANTUM AI AND MACHINE LEARNING | 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