Quantum machine learning is an emerging field, combining ideas from quantum computing and machine learning. In this paper we propose a novel quantum graph neural network for modelling financial time series. We demonstrate a proof of concept model implemented in pytorch-geometric and pennylane and benchmark the results on the Cora dataset. Further, we apply the model to financial time series data(NIFTY50) where we predict the movement of stock prices.
Quantum machine learning is an emerging field, combining ideas from quantum computing and machine learning. In this paper we propose a novel quantum graph neural network for modelling financial time series. We demonstrate a proof of concept model implemented in pytorch-geometric and pennylane and benchmark the results on the Cora dataset. Further, we apply the model to financial time series data(NIFTY50) where we predict the movement of stock prices.
The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application of QML methods—including variational quantum circuits, quantum kernel estimation, and quantum-enhanced support vector machines—for financial time-series prediction and asset price classification. We propose a hybrid quantum-classical framework that integrates quantum feature mapping with classical optimizers to improve model expressiveness and convergence. Empirical experiments are conducted using historical stock market data and synthetic datasets to benchmark QML approaches against established classical models such as long short-term memory networks and gradient boosting machines. The results demonstrate that QML techniques can achieve superior prediction accuracy and lower computational latency under certain data regimes, particularly when dealing with small-to-medium-sized datasets and high feature correlations. Additionally, the study examines scalability considerations, hardware constraints of near-term quantum devices, and the interpretability of quantum model outputs in financial decision-making contexts. The findings underscore the transformative potential of quantum machine learning as an innovative tool for advancing predictive analytics in finance and provide practical insights into how financial institutions can begin integrating QML capabilities into their workflows.
Financial markets generate complex, high-dimensional time series characterized by nonlinearity, noise, and structural uncertainty, posing persistent challenges for classical machine learning and statistical forecasting models. This paper examines quantum machine learning (QML) architectures for stock market forecasting, anomaly detection, and pattern recognition in financial time series. By leveraging quantum phenomena such as superposition, entanglement, and quantum-enhanced feature spaces, QML models offer new computational paradigms for capturing intricate market dynamics and latent structures. The study synthesizes recent advances in quantum neural networks, variational quantum circuits, and hybrid quantum–classical models, evaluating their applicability to predictive modeling and risk-sensitive detection tasks in finance. The paper argues that QML architectures have the potential to complement classical approaches by improving representational capacity and scalability for complex financial data, while also outlining current technical limitations and research directions necessary for practical deployment.
Financial markets, particularly equity indices like the SP 500, exhibit complex, nonlinear, and frequently chaotic behaviors driven by a combination of macroeconomic, geopolitical, and psychological factors. Traditional machine learning techniques, despite their notable success in modeling market trends, often encounter performance limitations when dealing with high-dimensional feature spaces and intricate historical dependencies. Quantum computing offers a promising approach for addressing these challenges through its ability to represent and process information in superposition states. This study proposes and implements a quantum neural network (QNN) architecture that utilizes angle encoding to embed carefully engineered features, including volatility measures, momentum indicators, and lagged price signals, into quantum states. These encoded features are processed through a customized quantum circuit comprising three variational layers that combine linear entanglement patterns with single-qubit rotation operations. The training process is conducted via gradient-based optimization through the PennyLane framework, operating within a noiseless simulation environment to establish theoretical performance bounds. The experimental results demonstrate that the QNN achieves an accuracy of 81.3% on the held-out test set in simulation, outperforming traditional baseline models including Long Short-Term Memory networks, Random Forest classifiers, and Support Vector Machines under identical conditions. The model exhibits balanced precision (0.83) and recall (0.79). While the experiments were conducted in simulation due to current quantum hardware limitations, the findings illustrate the potential of quantum-enhanced algorithms for financial forecasting applications. Keywords Quantum Neural Networks, Financial Forecasting, SP 500, Variational Quantum Circuits, Time Series Prediction.
The relentless quest for optimized financial portfolios has led to the exploration of advanced computational techniques capable of navigating financial markets’ complex and dynamic landscape. This dissertation presents a solution for portfolio optimization by harnessing the synergistic potential of quantum computing and machine learning. We propose a hybrid framework that integrates quantum algorithms’ optimization capabilities with machine learning’s predictive accuracy to address the multifaceted challenges of portfolio management. The study begins with a comprehensive review of traditional and contemporary portfolio optimization methods and an in-depth analysis of quantum computing principles relevant to optimization problems. Next, we examine machine learning models commonly used for forecasting financial time series, which serve as critical inputs for the quantum optimization process. The proposed solution is evaluated through simulations and real-world financial data to demonstrate its efficacy in achieving optimized asset allocations with enhanced risk-adjusted returns. This research contributes to the theoretical advancement of financial optimization techniques and provides practical insights for investors and portfolio managers seeking to leverage emerging technologies for strategic decision-making.
Modern quantum machine learning algorithms and techniques are reviewed in this review paper with possible financial applications. Along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs), we discuss QML techniques in supervised learning tasks like Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs). Risk management, credit scoring, fraud detection, and stock price prediction are among the financial applications that are taken into consideration. Additionally, we offer a summary of QML's drawbacks, possibilities, and restrictions in these particular domains as well as more widely throughout the field. With the help of this, we hope to provide data scientists, financial industry professionals, and enthusiasts with a quick overview of why quantum computing, and QML in particular, might be worthwhile to investigate in their respective fields of expertise.
Modern quantum machine learning algorithms and techniques are reviewed in this review paper with possible financial applications. Along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs), we discuss QML techniques in supervised learning tasks like Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs). Risk management, credit scoring, fraud detection, and stock price prediction are among the financial applications that are taken into consideration. Additionally, we offer a summary of QML's drawbacks, possibilities, and restrictions in these particular domains as well as more widely throughout the field. With the help of this, we hope to provide data scientists, financial industry professionals, and enthusiasts with a quick overview of why quantum computing, and QML in particular, might be worthwhile to investigate in their respective fields of expertise.