Quantum Algorithms for Materials Science: Studying quantum algorithms for chemistry and materials science applications
Keywords:
Quantum computing, Quantum algorithms, ChemistryAbstract
Quantum computing has emerged as a promising paradigm for tackling complex problems in chemistry and materials science that are intractable for classical computers. This paper provides an overview of quantum algorithms tailored for these fields, focusing on their potential to revolutionize molecular simulation, drug discovery, and material design. We discuss key quantum algorithms, such as the variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and quantum phase estimation (QPE), highlighting their principles and applications. Additionally, we explore quantum algorithms' current challenges and future prospects, emphasizing the need for continued research to realize their full potential.
Downloads
References
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.
Bojja, Giridhar Reddy, Jun Liu, and Loknath Sai Ambati. "Health Information systems capabilities and Hospital performance-An SEM analysis." AMCIS. 2021.
Vemoori, Vamsi. "Comparative Assessment of Technological Advancements in Autonomous Vehicles, Electric Vehicles, and Hybrid Vehicles vis-à-vis Manual Vehicles: A Multi-Criteria Analysis Considering Environmental Sustainability, Economic Feasibility, and Regulatory Frameworks." Journal of Artificial Intelligence Research 1.1 (2021): 66-98.
Jeyaraman, Jawaharbabu, and Muthukrishnan Muthusubramanian. "Data Engineering Evolution: Embracing Cloud Computing, Machine Learning, and AI Technologies." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1.1 (2023): 85-89.
Shahane, Vishal. "Investigating the Efficacy of Machine Learning Models for Automated Failure Detection and Root Cause Analysis in Cloud Service Infrastructure." African Journal of Artificial Intelligence and Sustainable Development2.2 (2022): 26-51.
Devan, Munivel, Ravish Tillu, and Lavanya Shanmugam. "Personalized Financial Recommendations: Real-Time AI-ML Analytics in Wealth Management." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)2.3 (2023): 547-559.
Abouelyazid, Mahmoud. "YOLOv4-based Deep Learning Approach for Personal Protective Equipment Detection." Journal of Sustainable Urban Futures 12.3 (2022): 1-12.
Prabhod, Kummaragunta Joel. "Leveraging Generative AI and Foundation Models for Personalized Healthcare: Predictive Analytics and Custom Treatment Plans Using Deep Learning Algorithms." Journal of AI in Healthcare and Medicine 4.1 (2024): 1-23.
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.
Althati, Chandrashekar, Manish Tomar, and Lavanya Shanmugam. "Enhancing Data Integration and Management: The Role of AI and Machine Learning in Modern Data Platforms." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2.1 (2024): 220-232.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.