Quantum-Hybrid AI Optimizes Portfolio Management with Adaptive Memory

The paper introduces Titans-QFWP, an innovative hybrid reinforcement learning framework designed for adaptive portfolio optimization. This architecture combines a Quantum Fast Weight Programmer with a unique memory system inspired by Titans, which incorporates Persistence, Surprise, and Forgetting mechanisms. The goal is to navigate the complexities of high-dimensional market features more effectively. To handle the vast amount of market data, the system employs an enhanced A3C^2 framework, utilizing Hungarian-aligned K-means clustering and scaled log-return rewards. When evaluated against 468 S&P 500 stocks, Titans-QFWP demonstrated robust performance, achieving competitive annual returns, Calmar ratios, and information ratios with a relatively small number of trainable parameters. A key finding from ablation studies is that quantum gating fundamentally alters the roles of the memory components. Persistence becomes crucial for drawdown control, Surprise contributes significantly to generating returns, and Forgetting provides additional market stabilization. This quantum-enhanced memory allows the model to make defensive allocations during market downturns while still capitalizing on growth opportunities.