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International Conference on IoT, Communication and Automation Technology (ICICAT)

Hybrid Quantum Deep Learning for Financial Market Trend Prediction

Rajat Kumar Dwibedi, L. K. Hema, Dhinil V Dhinesh, Amal A K, Ramamurthi Jayanthi, Neeraj Joshi

Published Dec 5, 2025• 0 citations
Publisher
DOI
10.1109/icicat68430.2025.11414552

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The need for smart, scalable algorithms to predict stock market movements has grown in response to the deluge of financial data coming from disparate sources including news articles, social media, and market pricing. Despite its success, traditional deep learning is ill-equipped to handle the complex nonlinear relationships and high-dimensional interactions found in financial markets. To get around these problems, this study presents a paradigm for quantum-enhanced multimodal intelligence that uses deep learning and quantum computing to predict stock market trends. Through the use of quantum-inspired feature mapping and quantumassisted optimisation, modality-specific encoders create representations from numerical market indicators and textual sentiment data. Both representation learning and exploring solution spaces are improved by this hybrid design. When tested on industry-standard financial datasets, the suggested model achieved better accuracy, precision, recall, and F1-score than typical multimodal deep learning baselines. The results of ablation studies further demonstrate that combining multimodal fusion with quantum enhancement improves the accuracy of predictions. Based on the findings, next-gen financial forecasting systems may incorporate quantum-augmented intelligence.
Prediction of stock price remains a challenging task because financial markets are dynamic and often non-linear, unpredictable patterns. To deal with such unpredictable data, the architecture that’s created must identify both time based trends and hidden patterns in data. In this study, we explore a hybrid modelling approach by combining both the classical deep learning techniques with quantum computing principles. Specifically, a Long Short-Term Memory (LSTM) is combined with a Quantum Neural Network (QNN) to understand the benefits of using quantum in time series forecasting. Rather than depending on a single quantum configuration, multiple feature maps and ansatz circuit designs were evaluated to identify the most suitable arrangement for the QLSTM model. Resultant framework blends ideas from both classical computing and quantum approaches to improve how we predict patterns over time in real-life scenarios.