Titans-QFWP: A Regime-Aware Hybrid Quantum Fast Weight Programmer for Portfolio Optimization

Titans-QFWP is a hybrid reinforcement learning architecture that combines a Quantum Fast Weight Programmer with a Titans-style memory system (Persistence, Surprise, and Forgetting) for adaptive portfolio optimization. It employs an enhanced A3C² framework with Hungarian-aligned K‑means clustering and scaled log‑return rewards to handle high‑dimensional market features. Tested on 468 S&P 500 stocks with about 3,000 trainable parameters, the model achieves strong performance metrics (median ARR 0.4260, Calmar 8.5504, IR 0.8427) and demonstrates that quantum gating reshapes memory roles to support drawdown control, return generation, and stabilization.

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• reinforcement-learning
• benchmarks