mlsanityData to 14 Oct 2026

HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction

arXiv 2503.15403 ↗First posted 19 Mar 2025q-fin.ST cs.LG quant-phcited by 10

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Abstract (arXiv)

Financial time-series forecasting remains a challenging task due to complex temporal dependencies and market fluctuations. This study explores the potential of hybrid quantum-classical approaches to assist in financial trend prediction by leveraging quantum resources for improved feature representation and learning. A custom Quantum Neural Network (QNN) regressor is introduced, designed with a novel ansatz tailored for financial applications. Two hybrid optimization strategies are proposed: (1) a sequential approach where classical recurrent models (RNN/LSTM) extract temporal dependencies before quantum processing, and (2) a joint learning framework that optimizes classical and quantum parameters simultaneously. Systematic evaluation using TimeSeriesSplit, k-fold cross-validation, and predictive error analysis highlights the ability of these hybrid models to integrate quantum computing into financial forecasting workflows. The findings demonstrate how quantum-assisted learning can contribute to financial modeling, offering insights into the practical role of quantum resources in time-series analysis.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 12 of its 56 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

12 of 56 references.

References · 12

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2018Supervised learning with quantum enhanced feature spacescited by 665
  3. 2020The power of quantum neural networkscited by 379
  4. 2019Parameterized quantum circuits as machine learning modelscited by 332
  5. 2016Quantum Machine Learningcited by 326
  6. 2017Quantum machine learning: a classical perspectivecited by 95
  7. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  8. 2023Improved Financial Forecasting via Quantum Machine Learningcited by 14
  9. 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Predictioncited by 9
  10. 2024Quantum Clustering for Cybersecuritycited by 6
  11. 2024Feature Importance and Explainability in Quantum Machine Learningcited by 4
  12. 2024SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systemscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 7.

10 papers.

  1. 2025Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogatescited by 3
  2. 2026Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecastingcited by 3
  3. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  4. 2026Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Predictioncited by 2
  5. 2025IQNN-CS: Interpretable Quantum Neural Network for Credit Scoringcited by 1
  6. 2026Hybrid Quantum-Classical Neural Architecture Searchcited by 1
  7. 2026A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inferencecited by 0
  8. 2026QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generationcited by 0
  9. 2026MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recoverycited by 0
  10. 2026Image Classification Using CNN-QNN Hybrid Model with Optimized Correlated Featurescited by 0