pone.0333068.t011 -

In the world of omnichannel retail, where customers seamlessly switch between online and offline channels, pricing and inventory management decisions have become more complex than ever. Customer purchasing behavior is influenced by uncertainty, market fluctuations, and competitive interactions, which traditional models fail to accurately predict. In such conditions, the need for intelligent and adaptive decision-making frameworks is more critical than ever. For the first time, this study presents a novel approach combining Quantum Decision Theory, Quantum Markov Chains (QMC), Quantum Dynamic Games, and Reinforcement Learning to optimize dynamic pricing and inventory management. By leveraging concepts such as superposition, observer effect, and quantum interference, the proposed model overcomes the limitations of classical models and provides a deeper understanding of customer behavior in uncertain environments. Additionally, a Quantum Multi-Level Markov Process (QMLMP) is employed to model market variations and enhance predictions. The results of this study demonstrate that the innovative model improves the accuracy of purchase behavior predictions, optimizes pricing and inventory management strategies, and helps retailers make more competitive and profitable decisions. This research introduces a transformative approach to tackling retail challenges in the digital age and paves the way for future studies in this domain.在全渠道零售（omnichannel retail）领域，消费者可在线上与线下渠道间无缝切换，定价与库存管理决策的复杂度也达到了前所未有的高度。消费者购买行为受不确定性、市场波动以及竞争互动的影响，而传统模型难以对其进行精准预测。在此背景下，对智能且自适应的决策框架的需求愈发迫切。本研究首次提出了一种融合量子决策理论、量子马尔可夫链（Quantum Markov Chains, QMC）、量子动态博弈与强化学习的创新方法，用于优化动态定价与库存管理。该模型借助叠加态、观测者效应以及量子干涉等概念，突破了经典模型的局限，能够更深入地理解不确定性环境下的消费者购买行为。此外，本研究还采用了量子多级马尔可夫过程（Quantum Multi-Level Markov Process, QMLMP）对市场波动进行建模，以提升预测精度。研究结果表明，该创新模型能够提升购买行为预测的准确率，优化定价与库存管理策略，助力零售商制定更具竞争力且盈利性更强的决策。本研究为应对数字时代的零售挑战提供了一种变革性方法，也为该领域的后续研究铺平了道路。