[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121352-en":3,"doc-seo-121352-105":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},121352,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Quantum Computing - Implications for Artificial Intelligence and Machine Learning","Quantum computing is presented as a disruptive technology for advancing artificial intelligence and machine learning. The work explains how qubits leverage superposition and entanglement to execute operations at quantum-fast speeds, enabling improvements in optimization, pattern detection, and deep-learning–related tasks. It reviews quantum computing fundamentals, contrasts them with conventional systems, and discusses modern progress alongside current obstacles and future prospects for this rapidly growing field, with emphasis on QAI and QML development.","Quantum Computing: Implications for Artificial Intelligence and Machine  \nLearning  \nDr. V. Rama Krishna 1, Ravi Kumar Jalli2, Kallakuri N V P S Brahma Ramesh3, Neerugatti  \nVaripally Vishwanath4, Neha Purohit5  \n1Associate Professor,CVR College Of Engineering, India, [rama.vishwa@gmail.com](rama.vishwa@gmail.com)[ ](rama.vishwa@gmail.com)2Assistant Professor, Departments of EEE, GMRIT, India, [ravikumar.j@gmrit.edu.in](ravikumar.j@gmrit.edu.in)[ ](ravikumar.j@gmrit.edu.in)3Assistant Professor, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), India, [ramesh.kb17@gmail.com](ramesh.kb17@gmail.com)[ ](ramesh.kb17@gmail.com)4Assistant Professor, Department of ECE, St.Martin's Engineering College, India,  \n[visu488@gmail.com](visu488@gmail.com)  \n5Assistant Professor, Department of Computer Science and Engineering, G H Raisoni College  \nof Engineering, India, [neha.purohit@raisoni.net](neha.purohit@raisoni.net)  \nAbstract: Quantum computing has transformed into a revolutionary technology which can revolutionize artificial intelligence (AI) and machine learning (ML) . The processing capabilities of quantum systems rely on the principles of superposition and entanglement to complete operations at quantum-fast speeds which drives solutions for optimization problems and pattern detection along with deep learning breakthroughs. The document analyzes quantum computing fundamentals and its leadership over conventional systems and their applications toward enhancing AI and ML capabilities. This paper examines modern advancements as well as  \npresent obstacles and future prospects ofthis fast-growing field.  \nKeywords: Quantum Computing, Artificial Intelligence, Machine  \nLearning, Superposition, Entanglement, Quantum Algorithms.  \n1. Introduction  \nArtificial intelligence (AI) with machine learning (ML) experienced remarkable advancement since the last few decades as it brought innovation to health care and finance and autonomous systems. AI and ML models become more complex to the point where they require greater computational power that encourages researchers to develop alternative computing paradigms better than classical machine capabilities [1-2] .  \nThe concept of quantum computing enables three key features: superposition and quantum parallelism as well as entanglement that allow quantum computers to process extensive data simultaneously. The unique property of quantum bits (qubits) enables superposition through which they maintain multiple states simultaneously since they differ from classical bits that function in 0 or 1 states. This feature allows quantum computing systems to solve problems at faster rates. The quantum benefits of processing have spawned substantial interest in quantum computing applications toward artificial intelligence and machine learning because they may transform deep learning operations and optimization methods and data analysis technologies [4-7] .  \nNovelty and Contribution  \nThis work pursues QAI development through an examination of the listed essential elements.  \nA. Comprehensive Analysis of Quantum Machine Learning (QML):  \nResearchers present a thorough examination of QNNs and QBMs alongside VQAs in their capacity to quicken ML operations through quantum methods. An evaluation of quantum ML vs classical ML demonstrates both advantages and drawbacks that exist between the approaches [9] .  \nB. Hybrid Quantum-Classical Approaches:  \nThe investigation shows how quantum computing combines with classical AI models to produce realistic applications on available quantum devices.  \nC. Real-World Applications and Future Directions:  \nThe paper surveys existing industry applications that connect quantum computing to AI/ML operations specifically in drug discovery and cryptography and financial modeling. This paper outlines next-generation quantum research approaches which focus on bettering quantum hardware capabilities and quantum error prevention and breakthrou","cbCaifXS5Yav8TBh","https://ap.wps.com/l/cbCaifXS5Yav8TBh","pdf",313654,1,9,"English","en",105,"# Introduction\n## Novelty and Contribution\n### Comprehensive Analysis of Quantum Machine Learning (QML)\n### Hybrid Quantum-Classical Approaches\n### Real-World Applications and Future Directions\n# Related Works","[{\"question\":\"How does quantum computing improve AI and machine learning compared with classical systems?\",\"answer\":\"Quantum computing relies on superposition and entanglement to process extensive data simultaneously, offering faster solutions for optimization and pattern detection tasks that are central to AI and ML.\"},{\"question\":\"What are the paper’s main contributions regarding quantum AI development?\",\"answer\":\"The paper focuses on comprehensive analysis of quantum machine learning methods, hybrid quantum-classical approaches for realistic deployment, and real-world applications with future research directions.\"},{\"question\":\"Which quantum machine learning approaches are discussed in the document?\",\"answer\":\"The document examines quantum neural networks (QNNs), quantum Boltzmann machines (QBMs), and variational quantum algorithms (VQAs), and compares quantum ML with classical ML to highlight benefits and drawbacks.\"}]","Quantum Computing - 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