Abstract
Artificial intelligence (AI) and Quantum Computing are two rapidly advancing technological areas, which can present new opportunities for designing intelligent systems with the ability to perform adaptive learning, especially when considering real-time applications that require significant computational capacity. With the advent of hybrid quantum–classical deep learning models: a completely new paradigm that combines well performing classical neural networks with computational advantages given by quantum mechanics phenomena such as superposition, entanglement and parallelism into one model type—models strong on both supervised a unsupervised tasks.
Abstract here, we address architectural design, optimization methods and real-time implementation of hybrid quantum–classical systems. Quantum circuits learn all the feature representations and speed up calculations which, in real-time under classical computation will become frustratingly intensive, however, deep learning frameworks scale up using computing resources running independently of quantum processes.
It investigates novel hybrid architectures including, e.g., Variational quantum circuits and deep neural networks hybrids and evaluates implemented applications of the new ones for intelligent real-time systems as e.g. autonomous vehicles or smart healthcare monitors as well industrial automations from now until at least October 2023. Novel approaches like quantum-informed training techniques and hybrid optimizers are effectively solving major problems in the industry including noise due to quantum hardware, data encoding constraints, and scalability limitations.
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