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HQNN-CL: Regime-Aware Hybrid Quantum Neural Network with Continual Learning for Financial Forecasting — arXiv:2611.02091

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02091
License (as archived): CC0-1.0
Archived: 2026-11-03T10:57:40.980Z
SHA-256 of the archived text: 7336f26e69d29b3da72c301dff2f6aa838755661764c07d3ec70431d46e92ce4
Stable URL of this page: https://metraipolis.com/documento/dc_efe2d42606aa

How to cite

HQNN-CL: Regime-Aware Hybrid Quantum Neural Network with Continual Learning for Financial Forecasting — arXiv:2611.02091 — arXiv (en), https://arxiv.org/abs/2611.02091 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_efe2d42606aa (Metraipolis archive dc_efe2d42606aa, sha256 7336f26e69d29b3da72c301dff2f6aa838755661764c07d3ec70431d46e92ce4)

BibTeX

@misc{dcefe2d42606aa,
  title = {HQNN-CL: Regime-Aware Hybrid Quantum Neural Network with Continual Learning for Financial Forecasting — arXiv:2611.02091},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02091},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_efe2d42606aa; sha256 7336f26e69d29b3da72c301dff2f6aa838755661764c07d3ec70431d46e92ce4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02091. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Financial time-series forecasting is still difficult owing to the non-stationary nature of the market, regime changes, and catastrophic forgetting, which makes conventional deep learning models hard to adapt in the long term. To achieve uncertainty-aware robust financial forecasting in the face of changing market conditions, this study proposes a Hybrid Quantum-Classical Neural Network with Continual Learning (HQNN-CL). The proposed framework combines Gaussian Hidden Markov Model (HMM) based regime detection, BiLSTM temporal encoding, adaptive-depth parameterized quantum circuits, Elastic Weight Consolidation (EWC), experience replay, and probabilistic forecasting in a single architecture. The framework was tested on four real-time stock datasets: Microsoft, Systems Limited, Teradata and Tata Consultancy Services, and compared with seven state-of-the-art forecasting models, including recurrent and Transformer-based models. The experimental results show that HQNN-CL has outperformed other methods on the Systems Limited (RMSE = 3.0149) and Teradata (RMSE = 0.8909) datasets and is within 0.32% and 0.40% of the best baseline method for Microsoft and Tata Consultancy Services, respectively. The proposed framework also generates well-calibrated probabilistic forecasts within the range of 89.5% to 96.5% empirical coverage rate, has zero catastrophic forgetting (BWT = 0.0000) for all sequential learning tasks, and consistently shows positive forward transfer. Ablation and statistical significance analyses also validate the performance of adaptive quantum feature learning and continual adaptation under heterogeneous market conditions, regime-wise.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02091 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:49:38.619Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).