mlsanityData to 14 Oct 2026

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

arXiv 2608.29093 ↗First posted 29 Aug 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 19 references that are in the map. First posted on arXiv 29 Aug 2026.

Builds on

9 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy15 Nov

Other references · 8

  1. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  2. 2017Deep Reinforcement Learning that Matterscited by 754
  3. 2020Variational Quantum Algorithmscited by 692
  4. 2024Titans: Learning to Memorize at Test Timecited by 328
  5. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  6. 2020Deep Learning for Financial Applications : A Surveycited by 91
  7. 2024Learning to Program Variational Quantum Circuits with Fast Weightscited by 24
  8. 2025Batched Training for QLSTM vs. QFWP: A System-Oriented Approach to EPC-Aware RMSE-DAcited by 2

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  1. 2025Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market
  2. 2026Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
  3. 2025Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading
  4. 2026Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations
  5. 2026Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
  6. 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction
  7. 2025Financial Decision Making using Reinforcement Learning with Dirichlet Priors and Quantum-Inspired Genetic Optimization
  8. 2025Contextual Quantum Neural Networks for Stock Price Prediction