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

arXiv: https://arxiv.org/abs/2503.15403 · HTML: https://mlsanity.com/paper/2503.15403/

## 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 (most cited first, 12 of 12)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Financial Fraud Detection using Quantum Graph Neural Networks](https://mlsanity.com/paper/2309.01127/) (2023)
- [Improved Financial Forecasting via Quantum Machine Learning](https://mlsanity.com/paper/2306.12965/) (2023)
- [QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction](https://mlsanity.com/paper/2408.03088/) (2024)
- [Quantum Clustering for Cybersecurity](https://mlsanity.com/paper/2408.02314/) (2024)
- [Feature Importance and Explainability in Quantum Machine Learning](https://mlsanity.com/paper/2405.08917/) (2024)
- [SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems](https://mlsanity.com/paper/2412.12731/) (2024)

## Built on by (most cited first, 10 of 10)

- [Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates](https://mlsanity.com/paper/2512.09586/) (2025)
- [Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting](https://mlsanity.com/paper/2606.27821/) (2026)
- [RobQFL: Robust Quantum Federated Learning in Adversarial Environment](https://mlsanity.com/paper/2509.04914/) (2025)
- [Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction](https://mlsanity.com/paper/2601.03802/) (2026)
- [IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring](https://mlsanity.com/paper/2510.15044/) (2025)
- [Hybrid Quantum-Classical Neural Architecture Search](https://mlsanity.com/paper/2605.18345/) (2026)
- [A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference](https://mlsanity.com/paper/2603.09789/) (2026)
- [QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation](https://mlsanity.com/paper/2606.04689/) (2026)
- [MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery](https://mlsanity.com/paper/2607.10707/) (2026)
- [Image Classification Using CNN-QNN Hybrid Model with Optimized Correlated Features](https://mlsanity.com/paper/2608.04379/) (2026)

## 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.