Kishore Kumar Sambangi 3 3 (July - September) Despite the dynamic nature of the market large dimensionalities of asset space and varieties of financial products portfolio optimization is a highly challenging problem These traditional methods like Markowitz MeanVariance Optimization and Risk Parity are highly static and have not been able to adjust to the speedofchange in market regimes In this paper the authors present a new QuantumInspired Portfolio Optimization QIPORL model that combines these interesting approaches to create a framework for a Reinforcement Learning RL based dynamic stock allocation algorithm The framework blends quantuminspired search techniques an adaptive RL agent and asset weights to maximize riskadjusted asset returns while maintaining assets in optimal allocation and provides a way to rebalance a portfolio continuously in response to the changing market environment The results from experiments were compared with state of the art baselines which showed that QIPORLs annual return is 168 its Sharpe ratio is 161 and the maximum drawdown is 115 which is the best among all the competing methods These findings support the synergism of using a global search method inspired by quantum computers in conjunction with an RLbased adaptation of decisions Copyright (c) International Journal of Computational Science and Engineering Research . All rights reserved 2026