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

TL;DR

This paper introduces a hybrid classical-quantum neural network for financial stock market prediction, demonstrating how quantum resources can enhance feature representation and improve forecasting accuracy in complex financial time-series data.

Contribution

It presents a novel quantum neural network regressor with a tailored ansatz and two hybrid optimization strategies for financial forecasting, advancing quantum-assisted time-series analysis.

Findings

Hybrid models improve prediction accuracy over classical methods

Quantum resources enhance feature representation in financial data

Systematic evaluation confirms practical viability of quantum-assisted forecasting

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.