Quantum Machine Learning: A Paradigm Shift in AI Computation
Abstract
The convergence of quantum computing and machine learning has the potential to revolutionize computational efficiency and problem-solving capabilities. This paper explores quantum machine learning (QML) techniques, focusing on quantum-enhanced neural networks and quantum kernel methods for classification tasks. We benchmark the performance of quantum algorithms against classical ML models on real-world datasets, highlighting speedups in optimization and pattern recognition. Our findings suggest that QML can significantly improve model efficiency for complex problems, paving the way for advancements in AI-driven scientific research and real-time decision-making.
References
Balantrapu, S. S. (2022). Evaluating AI-Enhanced Cybersecurity Solutions Versus Traditional Methods: A Comparative Study. International Journal of Sustainable Development Through AI, ML and IoT, 1(1), 1-15.
Balantrapu, S. S. (2022). Ethical Considerations in AI-Powered Cybersecurity. International Machine learning journal and Computer Engineering, 5(5).
Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.
Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.
Balantrapu, S. S. (2023). Evaluating the effectiveness of machine learning in phishing detection. International Scientific Journal for Research, 5(5).
Balantrapu, S. S. (2024). A Comprehensive Review of AI Applications in Cybersecurity. International Machine learning journal and Computer Engineering, 7.
S. V. N. Sreenivasu, S. K. Katta, J. P. L. Auguskani, D. V. Priya, V. Jagadish and V. Raghunath, "Integrating AI-Driven IoT Solutions for Enhanced Predictive Analytics in Healthcare a Comprehensive Study on Chronic Disease Management," 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC), Guntur, India, 2024, pp. 1-6, doi: 10.1109/ICEC59683.2024.10837020.
R. R. Yasani, P. M. Prasad, P. Srinivas, N. V. R. S. Reddy, P. Jawarkar and V. Raghunath, "AI-Driven Solutions for Cloud Security Implementing Intelligent Threat Detection and Mitigation Strategies," 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC), Guntur, India, 2024, pp. 1-6, doi: 10.1109/ICEC59683.2024.10837032.
V. Raghunath, "Security Issues Analysis Based on Big Data in Cloud Computing," World Journal of Advanced Research and Reviews, vol. 23, no. 3, pp. 2549-2557, 2024.
V. Raghunath, "Analysis on Addressing the Threats to Cloud Computing on the Basis of Security Safeguards for SAP Cloud Services," World Journal of Advanced Research and Reviews, vol. 23, no. 3, pp. 2539-2548, 2024.
V. Raghunath, "SAP S/4HANA Applications on Data Security and Protections for SAP Cloud Services," World Journal of Advanced Research and Reviews, vol. 23, no. 3, pp. 2530-2538, 2024.
V. Raghunath, "Investigation on Cloud Security Frameworks, Problems and Proposed Solutions," European Journal of Advances in Engineering and Technology, vol. 11, no. 9, pp. 103-110, 2024
V. Raghunath, "SAP Cloud Services on Decoding Cybersecurity and Data Privacy Controls," European Journal of Advances in Engineering and Technology, vol. 11, no. 9, pp. 95-102, 2024.
V. Raghunath, "A Comprehensive Review on Security and Privacy Properties in Cloud-Based Business and Scientific Workflows," European Journal of Advances in Engineering and Technology, vol. 11, no. 9, pp. 87-94, 2024.
V. Raghunath, "Analysis on the Planning Feature of SAP Analytics Cloud with Artificial Intelligence and Machine Learning Algorithms," International Journal of Artificial Intelligence & Machine Learning (IJAIML), 2024.
V. Raghunath, "User Support Solution Implementation for SAP ERP Utilizing Artificial Intelligence to Drive Automated," International Journal of Artificial Intelligence Research and Development, 2024.
V. Raghunath, "Predictive Analytics on SAP Database (HANA) by Using Artificial Intelligence (AI) and Automated Machine Learning Capabilities," International Journal of Computer Engineering and Technology (IJCET), vol. 15, no. 3, 2024.
V. Raghunath, "Investigating the Adaptive Supply Chain Module for the Integration of Google Cloud and SAP HANA Technologies," International Journal of System Design and Information Processing (IJSDIP), 2024
Mettikolla, P. (2023). Familial Hypertrophic Cardiomyopathy and Sustainable Healthcare: Genetic Insights, Clinical Implications, and Future Therapeutic Strategies for Global Health. International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-25.
Mettikolla, P., Balammal, G., & Meena, D. (2022). The effect of sun light exposure on prediabetic patients in tamil nadu population. International Journal of Pharmaceutical Research and Life Sciences, 10(2), 30-35.
Mettikolla, P., & Umasankar, K. (2019). Epidemiological analysis of extended-spectrum β-lactamase-producing uropathogenic bacteria. International Journal of Novel Trends in Pharmaceutical Sciences, 9(4), 75-82.
Dr. Prasad Mettikolla, Dr. T. Sunil Kumar Reddy, & Dr. G. Balammal. (2024). EXPLORING COX-1 AND COX-2 INHIBITION POTENTIAL OF AMBERBOA DIVARICATA AERIAL PARTS THROUGH IN-SILICO AND IN-VITRO STUDIES. Journal of Population Therapeutics and Clinical Pharmacology, 31(11), 292-298.
Dr. A. Saravana Kumar Dr. Prasad Mettikolla.(2014). IN VITRO ANTIOXIDANT ACTIVITY ASSESSMENT OF CAPPARIS ZEYLANICA FLOWERS. International Journal of Phytopharmacology, 5(6), 496-501.
Dr. R. Gandhimathi Dr. Prasad Mettikolla.(2015). EVALUATION OF ANTINOCICEPTIVE EFFECTS OF MELIA AZEDARACH LEAVES. International Journal of Pharmacy, 5(2), 104-108.
G. Sangeetha Dr. Prasad Mettikolla.(2016). ASSESSMENT OF IN VITRO ANTI-DIABETIC PROPERTIES OF CATUNAREGAM SPINOSA EXTRACTS. International Journal of Pharmacy Practice & Drug Research, 6(2), 76-81.
