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Intelligent systems reference library

Advances in Quantum Inspired Artificial Intelligence

Satchidananda Dehuri, Monalisa Jena, Sarat Chandra Nayak, Margarita N. Favorskaya, Smaranda Belciug

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.1007/978-3-031-89905-8

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This paper introduces a new method that combines Artificial Intelligence (AI) and quantum-inspired techniques to improve the efficiency of multi-variable optimization experiments. By using advanced software simulations, this approach significantly reduces the time and cost compared to traditional physical experiments. The research focuses on enzyme fermentation, demonstrating that this method can achieve better results with fewer experiments. The findings highlight the potential of this approach to more effectively identify optimal formulations, leading to advancements in enzyme fermentation and other fields that require complex optimization. Initially, the Active Ingredients (AIN) could not be improved even after 600 experiments. However, by adopting the method outlined in this paper, we were able to identify a better formula in just 405 experiments. This resulted in an increase of AIN from 8481 to 10068, representing an improvement of 18.7%.
The rapid advancement of artificial intelligence (AI) and quantum computing has introduced new paradigms for solving computationally complex problems beyond the capabilities of classical systems. This paper presents a Quantum Intelligence Platform (QIP), a hybrid quantum-inspired computational framework designed to enhance intelligent decision prediction and optimization in complex environments. The proposed system integrates quantum probabilistic modelling concepts such as superposition and interference with classical machine learning algorithms to improve prediction accuracy and adaptive decision-making. A hybrid architecture combining quantum circuit simulation and classical deep learning modules is developed to enable scalable experimentation without requiring physical quantum hardware. The platform is evaluated on decision prediction and optimization tasks, demonstrating improvements in convergence stability and predictive performance compared to conventional models. Experimental results show that quantum-inspired state representation enhances feature expressiveness and probabilistic reasoning. The proposed framework bridges the gap between theoretical quantum computation and practical intelligent systems, offering a scalable pathway for future quantum-enhanced AI applications.