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International Journal for Research in Applied Science and Engineering Technology

Quantum-Inspired Dynamic Decision-Making Algorithm (QIDDM): A Robust Framework for Delayed Commitment in Uncertain Environments

Mr. Mahesh Y. Gondekar

Published Mar 13, 2025• 0 citations
Publisher

Abstract

This paper introduces the Quantum-Inspired Dynamic Decision-Making Algorithm (QIDDM), a novel framework that leverages quantum mechanical principles-superposition, entanglement, and collapse-to optimize decision-making in dynamic, uncertain environments. By maintaining a probabilistic superposition of potential actions until contextual data triggers a collapse, QIDDM delays premature commitments, enabling adaptive responses in robotics, finance, and reinforcement learning. Experimental validation in simulated robotic navigation and financial trading environments demonstrates a 27% improvement in decision accuracy and 33% reduction in premature commitments compared to classical threshold-based and reinforcement learning methods. The algorithm’s mathematical formalism, scalability, and real-world applicability are rigorously analyzed, establishing it as a transformative approach for dynamic decision-making.

DOI
10.22214/ijraset.2025.67375

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This paper introduces the Quantum-Inspired Dynamic Decision-Making Algorithm (QIDDM), a novel framework that leverages quantum mechanical principles-superposition, entanglement, and collapse-to optimize decision-making in dynamic, uncertain environments. By maintaining a probabilistic superposition of potential actions until contextual data triggers a collapse, QIDDM delays premature commitments, enabling adaptive responses in robotics, finance, and reinforcement learning. Experimental validation in simulated robotic navigation and financial trading environments demonstrates a 27% improvement in decision accuracy and 33% reduction in premature commitments compared to classical threshold-based and reinforcement learning methods. The algorithm’s mathematical formalism, scalability, and real-world applicability are rigorously analyzed, establishing it as a transformative approach for dynamic decision-making.
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This paper investigates the potential of quantum-inspired algorithms to improve reinforcement learning (RL) policy optimization. Traditional RL methods often struggle with slow convergence and exploration challenges, particularly in complex environments. We propose a novel approach that draws inspiration from quantum mechanics, specifically superposition and entanglement, to address these limitations. Our algorithms aim to exploit superposition to simultaneously evaluate multiple action possibilities and utilize entanglement to model correlations between states, thereby accelerating the learning process. We present a framework for incorporating these concepts into RL, focusing on algorithmic design rather than a direct quantum simulation. The theoretical analysis suggests that our approach could significantly enhance exploration efficiency and, consequently, policy convergence rates. This work represents a direct application of quantum-inspired ideas to RL policy optimization, offering a potentially transformative pathway for developing more efficient and robust RL agents.
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