A Quantum Machine Learning Approach for Bridging the Gap Between Quantum and Classical Computing. - Surjeet, Surjeet; Bulla, Chetan; Arya, Amit; Idrees, Sumaira; Singh, Gurwinder; Rao, S. Govind; Shirahatti, Abhinandan (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The relentless march of technological progress has ushered in an era where the computational boundaries between quantum and classical systems have become increasingly distinct. This research paper endeavors to traverse this schism, proposing innovative approaches aimed at bridging the gap between quantum and classical computing paradigms. The burgeoning capabilities of quantum computing hold promise for solving complex problems that elude classical systems; however, practical implementation faces formidable challenges. Through an in-depth exploration of the current landscape, this research identifies key gaps and issues impeding the seamless integration of quantum and classical computing. Our methodology involves a comprehensive review of existing approaches, coupled with theoretical modeling and empirical investigations where applicable. The results unveil novel insights into the convergence of quantum and classical computing, offering a nuanced understanding of the intricate interplay between these computational realms. The discussion interprets these findings, examining their implications for advancing technology and overcoming limitations in both quantum and classical computing. While acknowledging the inherent limitations of our study, we propose avenues for future research, envisioning a harmonious coalescence of quantum and classical computing that unlocks unprecedented computational power. This research contributes to the ongoing dialogue on the frontiers of computing, presenting a compelling case for the symbiotic evolution of quantum and classical computational methodologies.