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

2025-03-19 · Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique, Rajeev Singh

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.

📄 PDF Abstract BibTeX arXiv:2503.15403

Code (0)

등록된 구현이 없습니다.

Tasks

Stock Market PredictionTime SeriesTime Series Analysis Time Series Forecasting

FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report

2025-07-25 · Matteo Cardaioli, Luca Marangoni, Giada Martini, Francesco Mazzolin 외 arxiv

The increasing complexity and volume of financial transactions pose significant challenges to traditional fraud detection systems. This technical report investigates and compares the efficacy of classical, quantum, and q…

Quantum Machine Learning Binary Classification Feature Engineering Fraud Detection

Hybrid Quantum-Classical Neural Architecture Search

2026-05-18 · Alberto Marchisio, Muhammad Kashif, Nouhaila Innan, Muhammad Shafique arxiv

Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combine classical learning components with pa…

Neural Architecture Search Quantum Machine Learning

FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithm

2025-11-13 · Muhammad Kashif, Shaf Khalid, Alberto Marchisio, Nouhaila Innan 외 arxiv

Hybrid Quantum Neural Networks (HQNNs), which combine parameterized quantum circuits with classical neural layers, are emerging as promising models in the noisy intermediate-scale quantum (NISQ) era. While quantum circui…

Neural Architecture Search

Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks

2026-05-22 · Guilherme Cruz, Nouhaila Innan, Alberto Marchisio, Gabriel Falcao 외 arxiv

Accurate classification of microscopic blood cells is still a critical task in medical image analysis, where subtle variations and limited data can challenge conventional deep learning models. As such, we investigate in …

Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio

2024-03-04 · Aiswariya Sweety Malarvanan

In this paper, we investigate the performance of a Hybrid Quantum Neural Network (HQNN) and a comparable classical Convolution Neural Network (CNN) for detection and classification problem using a radar. Specifically, we…

Time Series