Quantum Computing and Deep Learning Integration: Challenges and Opportunities
Keywords:
Computational Intelligence; Deep Learning; Hybrid Framework; Quantum Computing, Quantum-Deep Learning Interface, Quantum Advantage.Abstract
This study introduces a groundbreaking hybrid framework integrating quantum computing (QC) into deep learning for efficient fault diagnosis in electrical power systems. Leveraging the strengths of conditional restricted Boltzmann machines and deep networks, our approach addresses computational challenges through novel QC-based training methodologies. The research unfolds in seven phases, from quantum computing infrastructure to statistical analysis, showcasing the implementation of a cutting-edge quantum processor, TensorFlow-based deep learning, and Quantum-Deep Learning Interface. Results demonstrate a quantum advantage in accuracy, efficiency, and training time reduction. Challenges and opportunities highlight the need for technological maturity, algorithmic complexity solutions, and seamless quantum-classical system interfacing. Future scope encompasses refining algorithms, broadening use cases, and collaborating for responsible deployment. This work marks a transformative step towards computational intelligence, contributing to the synergy of quantum computing and deep learning.
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