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[2503.01884] Contextual Quantum Neural Networks for Stock Price Prediction
Summary
This article presents a novel approach using contextual quantum neural networks for predicting stock prices, enhancing accuracy and efficiency in financial modeling.
Why It Matters
The integration of quantum machine learning in finance represents a significant advancement in predictive analytics, potentially transforming investment strategies and financial forecasting. This research highlights the ability to capture inter-asset correlations and improve prediction accuracy, which is crucial for investors and financial analysts.
Key Takeaways
- Introduces a new training technique, quantum batch gradient update (QBGU), enhancing convergence in quantum applications.
- Proposes a quantum multi-task learning architecture that efficiently trains multiple assets simultaneously.
- Demonstrates superior predictive power over traditional models using extensive experimentation on S&P 500 data.
Computer Science > Machine Learning arXiv:2503.01884 (cs) [Submitted on 26 Feb 2025 (v1), last revised 15 Feb 2026 (this version, v2)] Title:Contextual Quantum Neural Networks for Stock Price Prediction Authors:Sharan Mourya, Hannes Leipold, Bibhas Adhikari View a PDF of the paper titled Contextual Quantum Neural Networks for Stock Price Prediction, by Sharan Mourya and Hannes Leipold and Bibhas Adhikari View PDF HTML (experimental) Abstract:In this paper, we apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network. Our approach captures recent trends to predict future stock price distributions, moving beyond traditional models that focus on entire historical data, enhancing adaptability and precision. Utilizing the principles of quantum superposition, we introduce a new training technique called the quantum batch gradient update (QBGU), which accelerates the standard stochastic gradient descent (SGD) in quantum applications and improves convergence. Consequently, we propose a quantum multi-task learning (QMTL) architecture, specifically, the share-and-specify ansatz, that integrates task-specific operators controlled by quantum labels, enabling the simultaneous and efficient training of multiple assets on the same quantum circuit as well as enabling efficient portfolio representation with logarithmic overhead in the number of qubits. This architecture represents the first of its kind in quantum finance, ...
