Titans-QFWP: A Regime-Aware Hybrid Quantum Fast Weight Programmer for Portfolio Optimization
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Computer Science > Machine Learning
Title:Titans-QFWP: A Regime-Aware Hybrid Quantum Fast Weight Programmer for Portfolio Optimization
Abstract:We propose Titans-QFWP, a hybrid reinforcement learning architecture integrating a Quantum Fast Weight Programmer with Titans-style memory (Persistence, Surprise, and Forgetting) for adaptive portfolio optimization. To address high-dimensional market features, we introduce an enhanced A3C^2 framework with Hungarian-aligned K-means clustering and scaled log-return rewards. Evaluated on 468 S&P 500 stocks under an Equal-Parameter-Count (EPC) benchmark with approximately 3,000 trainable parameters, Titans-QFWP achieves strong performance (median ARR 0.4260, Calmar 8.5504, IR 0.8427). Ablation results reveal that quantum gating fundamentally reshapes memory component roles, with Persistence supporting drawdown control, Surprise contributing to return generation, and Forgetting providing additional stabilization. By stabilizing these quantum representations, the model enables defensive allocation during market drawdowns while preserving upside potential.
| Comments: | 4 pages, 3 figures, 4 tables, accepted for presentation at the IEEE International Conference on Quantum Computing and Engineering (QCE) 2026 QCRL Workshop |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.29093 [cs.LG] |
| (or arXiv:2608.29093v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29093
arXiv-issued DOI via DataCite (pending registration)
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