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

ArXiv ID: 2503.15403 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Quantum Neural Networks (QNN), Time-Series Forecasting, Hybrid Quantum-Classical Algorithms, Financial Trend Prediction, Deep Learning, Equities (Quantitative Analysis)

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats
  • Why: The paper presents novel quantum machine learning architectures with significant mathematical foundations in quantum mechanics and variational methods, but its empirical evaluation is based on conceptual visualizations and methodology descriptions without detailed backtesting, code, or specific financial datasets.
  flowchart TD
    A["Research Goal:<br/>Hybrid Quantum-Classical<br/>Financial Stock Prediction"] --> B["Data<br/>Historical Stock Time-Series"]
    
    B --> C{"Methodology"}
    
    C --> D["Sequential Hybrid<br/>RNN/LSTM + QNN"]
    C --> E["Joint Learning<br/>Simultaneous Optimization"]
    
    D --> F["Training & Validation<br/>TimeSeriesSplit & K-Fold"]
    E --> F
    
    F --> G["Key Findings<br/>Quantum-enhanced feature representation<br/>Validated hybrid workflows<br/>Practical quantum utility in finance"]