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- W4379792011 abstract "Abstract In this groundbreaking investigation, we delve into the fertile intersection of quantum mechanics and deep learning, introducing a unique hybrid quantum-classical approach that holds promising implications for the advancement of Artificial Intelligence (AI). Expanding upon the Deep Learning algorithm, we put forth Quantum Deep Learning (QDL), an innovative methodology that synergistically intertwines quantum principles with deep learning algorithms, facilitated by simulation on classical computational platforms. Through this intricate blend, we aim to harness the colossal computational prowess of quantum mechanics, setting the stage for unprecedented improvements in AI model performance. This research paves the way for a transformative future in AI, where quantum-inspired deep learning techniques could redefine the landscape of AI applications." @default.
- W4379792011 created "2023-06-09" @default.
- W4379792011 creator A5063265276 @default.
- W4379792011 date "2023-06-08" @default.
- W4379792011 modified "2023-09-25" @default.
- W4379792011 title "Hybrid Quantum-Classical Approach: Quantum-Inspired Deep Learning Using Classical Simulation" @default.
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- W4379792011 doi "https://doi.org/10.21203/rs.3.rs-3021644/v1" @default.
- W4379792011 hasPublicationYear "2023" @default.
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