A Quantum-Edge Deep Reinforcement Learning Framework For Adaptive And Privacy-Preserving Dynamic Pricing In E-commerce

The rapid rise of e-commerce platforms has created a need for complex pricing systems that react to market conditions in real-time to improve market share and customer satisfaction. In this paper, we present a new Edge-AI powered situational pricing optimization framework based on a Deep Reinforcement Learning (DRL) model, leveraging the low latency pricing decision-making capability of a distributed edge computing network. In our model, we use federated learning processes with multi-agent deep reinforcement learning to create hybrid pricing intelligence based on the ongoing analysis of patterns of customer behaviour, competitors and market volatility signals. Our framework offers a solution to the fundamental limitations of cloud-based traditional pricing systems (and understandings) in shipping complex processes to ultra-sophisticated AI pricing engines that function on lightweight AI models located at edge nodes in the network, improving latency from seconds to milliseconds. Our experimental validation based on real e-commerce data shows a 23.4% im-provement in revenue optimizations, 18.7% improvements in reduction for de-cision latency of price adjustments and a remarkable 31.2% increase in customer satisfaction metrics relative to the previous centralized mode (cloud-based). This system offers a decentralized framework that can scale globally to support multi-market e-commerce operations, while also improving data privacy and confidential processing in compliance with regulatory demands.