Contextual Quantum Neural Networks for Stock Price Prediction

2025-02-26

AI Summary

This work addresses the limitations of conventional stock price forecasting models—namely, their overreliance on historical data and poor adaptability—by proposing a context-aware quantum multi-task learning framework. Methodologically, we introduce a novel “share-and-specify” parameterized quantum circuit ansatz, integrated with a Quantum Batch Gradient Update (QBGU) algorithm, enabling joint modeling of multiple assets while emphasizing recent market trends; this design reduces quantum resource overhead logarithmically. Experiments on Apple, Google, Microsoft, and Amazon stock prediction demonstrate superior accuracy and faster convergence compared to quantum single-task baselines, alongside accurate capture of cross-asset dynamic correlations. The core contributions are: (i) the first quantum multi-task learning architecture tailored for financial time series, and (ii) QBGU—a dedicated quantum optimization mechanism. Together, they establish a new paradigm for lightweight, adaptive quantum modeling of high-dimensional financial time series.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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, offering superior predictive power and computational efficiency for multi-asset stock price forecasting. Through extensive experimentation on S&P 500 data for Apple, Google, Microsoft, and Amazon stocks, we demonstrate that our approach not only outperforms quantum single-task learning (QSTL) models but also effectively captures inter-asset correlations, leading to enhanced prediction accuracy. Our findings highlight the transformative potential of QML in financial applications, paving the way for more advanced, resource-efficient quantum algorithms in stock price prediction and other complex financial modeling tasks.

Problem

• Predict stock prices using quantum machine learning.
• Enhance adaptability and precision in stock prediction.
• Enable efficient multi-asset training on quantum circuits.

Innovation

• Quantum batch gradient update accelerates SGD.
• Quantum multi-task learning integrates task-specific operators.
• Share-and-specify ansatz enables efficient multi-asset training.