Quantum Computing Platforms - Development and Integration: Investigating the development and integration of quantum computing platforms for building scalable and fault-tolerant quantum computers
Keywords:
Quantum computing, quantum computing platforms, qubitsAbstract
Quantum computing has emerged as a promising paradigm that could revolutionize computation by solving complex problems exponentially faster than classical computers. Central to the advancement of quantum computing are the development and integration of quantum computing platforms. These platforms are essential for realizing scalable and fault-tolerant quantum computers, which can perform computations beyond the capabilities of classical computers. This paper provides a comprehensive overview of the current state of quantum computing platforms, focusing on their development and integration strategies. We discuss key components of quantum computing platforms, such as qubits, quantum gates, and quantum error correction, and explore various approaches to building these components. Additionally, we examine the challenges associated with integrating these components into a coherent quantum computing system and discuss potential solutions. Finally, we highlight the potential applications of quantum computing platforms and outline future directions for research in this field.
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References
Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
Tatineni, Sumanth, and Anirudh Mustyala. "Advanced AI Techniques for Real-Time Anomaly Detection and Incident Response in DevOps Environments: Ensuring Robust Security and Compliance." Journal of Computational Intelligence and Robotics 2.1 (2022): 88-121.
Biswas, A., and W. Talukdar. “Robustness of Structured Data Extraction from In-Plane Rotated Documents Using Multi-Modal Large Language Models (LLM)”. Journal of Artificial Intelligence Research, vol. 4, no. 1, Mar. 2024, pp. 176-95, https://thesciencebrigade.com/JAIR/article/view/219.
Sontakke, Dipti, and Mr Pankaj Zanke. "Quality Analytics and Customer Satisfaction: Insights from Retail Industry." Available at SSRN 4847927 (2024).
Ponnusamy, Sivakumar, and Pankaj Gupta. "Connecting the Dots: How Data Lineage Helps in Effective Data Governance."
Bojja, Giridhar Reddy, and Jun Liu. "Impact of it investment on hospital performance: a longitudinal data analysis." (2020).
Singh, Amarjeet, and Alok Aggarwal. "Microservices Security Secret Rotation and Management Framework for Applications within Cloud Environments: A Pragmatic Approach." Journal of AI-Assisted Scientific Discovery 3.2 (2023): 1-16.
Shahane, Vishal. "Optimizing Cloud Resource Allocation: A Comparative Analysis of AI-Driven Techniques." Advances in Deep Learning Techniques 3.2 (2023): 23-49.
Vemoori, Vamsi. "Harnessing Natural Language Processing for Context-Aware, Emotionally Intelligent Human-Vehicle Interaction: Towards Personalized User Experiences in Autonomous Vehicles." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 53-86.
Tillu, Ravish, Muthukrishnan Muthusubramanian, and Vathsala Periyasamy. "Transforming regulatory reporting with AI/ML: strategies for compliance and efficiency." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.1 (2023): 145-157.
Bayani, Samir Vinayak, Ravish Tillu, and Jawaharbabu Jeyaraman. "Streamlining Compliance: Orchestrating Automated Checks for Cloud-based AI/ML Workflows." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.3 (2023): 413-435.
Tomar, Manish, and Vathsala Periyasamy. "Leveraging advanced analytics for reference data analysis in finance." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.1 (2023): 128-136.
Abouelyazid, Mahmoud. "Advanced Artificial Intelligence Techniques for Real-Time Predictive Maintenance in Industrial IoT Systems: A Comprehensive Analysis and Framework." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 271-313.
Prabhod, Kummaragunta Joel. "AI-Driven Insights from Large Language Models: Implementing Retrieval-Augmented Generation for Enhanced Data Analytics and Decision Support in Business Intelligence Systems." Journal of Artificial Intelligence Research 3.2 (2023): 1-58.
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
Gupta, Pankaj, and Sivakumar Ponnusamy. "Beyond Banking: The Trailblazing Impact of Data Lakes on Financial Landscape." International Journal of Computer Applications 975: 8887.
Zanke, Mr Pankaj, and Dipti Sontakke. "The Impact of Business Intelligence on Organizational Performance." Available at SSRN 4847945 (2024).
Shahane, Vishal. "Evolving Data Durability in Cloud Storage: A Historical Analysis and Future Directions." Journal of Science & Technology 1.1 (2020): 108-130.
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