Financial markets are complex nonlinear systems characterized by high-dimensional interactions, temporal dependencies, and rapidly evolving asymmetric patterns. Detecting hidden structures and abnormal behaviors in high-frequency financial data remains a challenging problem due to market instability and dynamic uncertainty. This study proposes an innovative quantum neural network (QNN)-based framework for adaptive forecasting and anomaly detection by exploiting quantum representation learning for complex financial systems. The proposed architecture is developed in the Qiskit environment using a hybrid classical-quantum optimization strategy based on a Variational Quantum Circuit (VQC). A quantum feature-mapping mechanism transforms multidimensional financial variables into quantum states, enabling extraction of latent relationships and structural patterns from high-dimensional data. A parameterized quantum circuit is optimized using the ADAM algorithm within a classical-quantum learning loop. The framework consists of two stages: first, multivariate financial dynamics are forecasted using market microstructure, volatility-related, and macroeconomic indicators obtained from the Electronic Data Distribution System (EDDS) of the Central Bank of the Republic of Türkiye; second, deviations between predicted and observed states are analyzed for anomaly identification. Comprehensive experiments on the BIST All-100 Index, trading volume, and monetary indicators demonstrate that the proposed QNN model achieves superior performance compared with ANN, LSTM, Bi-LSTM, LSTM-GRU, CNN, and Transformer-based approaches. The findings indicate that quantum-enhanced learning provides an effective computational framework for modeling complex asymmetric patterns and discovering hidden structures in dynamic systems.
Financial markets are complex nonlinear systems characterized by high-dimensional interactions, temporal dependencies, and rapidly evolving asymmetric patterns. Detecting hidden structures and abnormal behaviors in high-frequency financial data remains a challenging problem due to market instability and dynamic uncertainty. This study proposes an innovative quantum neural network (QNN)-based framework for adaptive forecasting and anomaly detection by exploiting quantum representation learning for complex financial systems. The proposed architecture is developed in the Qiskit environment using a hybrid classical-quantum optimization strategy based on a Variational Quantum Circuit (VQC). A quantum feature-mapping mechanism transforms multidimensional financial variables into quantum states, enabling extraction of latent relationships and structural patterns from high-dimensional data. A parameterized quantum circuit is optimized using the ADAM algorithm within a classical-quantum learning loop. The framework consists of two stages: first, multivariate financial dynamics are forecasted using market microstructure, volatility-related, and macroeconomic indicators obtained from the Electronic Data Distribution System (EDDS) of the Central Bank of the Republic of Türkiye; second, deviations between predicted and observed states are analyzed for anomaly identification. Comprehensive experiments on the BIST All-100 Index, trading volume, and monetary indicators demonstrate that the proposed QNN model achieves superior performance compared with ANN, LSTM, Bi-LSTM, LSTM-GRU, CNN, and Transformer-based approaches. The findings indicate that quantum-enhanced learning provides an effective computational framework for modeling complex asymmetric patterns and discovering hidden structures in dynamic systems.
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.
High-Frequency Financial Forecasting is important in algorithmic trading as it helps make decisions based on high-frequency data in milliseconds. This challenge has been found to be unsolvable using conventional machine learning approaches, such as classical Support Vector Machines (SVMs), Random Forest (RF), and deep learning models, which were not able to capture the nonlinear and high-dimensional patterns embedded in the high-frequency datasets, leading to poor prediction performance. This paper proposes a Supervised Quantum Support Vector Machine (QSVM) model, which is tailored for the high-frequency financial forecasting task. The QSVM can effectively encode the market information and enhance the predictability while maintaining the computational efficiency through quantum feature mapping and high-dimensional Hilbert space. This process includes encoding of tick-level market microstructure data, stock and futures prices, and technical indicators into quantum states and subsequent training using a quantum-classical hybrid optimization loop. Classical SVM, Random Forest, LSTM, and hybrid QML are used as the benchmarks for the proposed QSVM using forecasting accuracy, RMSE, MAPE, computational latency, and Sharpe ratio. The results obtained from the experiment show that the QSVM has better accuracy (91.2%), RMSE (0.018), and latency (22 ms) as well. Hence, the QSVM shows better performance when compared to other methods and is capable of modeling the markets that have a fast change in dynamics. High-dimensional quantum kernel plays a part in enhancing the ability to separate non-linearities. Therefore, the QSVM could be a potential solution to apply in algorithmic trading. The limitations are hardware constraints of NISQ and restrictions on the size of the dataset. Future directions of research will include the scalability of the model and the use of ensemble approaches.
Financial markets in the MENA region face critical challenges in detecting anomalies within high-frequency trading and cross-border payment systems, where traditional machine learning approaches struggle with extreme dimensionality, class imbalance, and real-time constraints.This paper presents a theoretical quantum-enhanced deep learning framework addressing these challenges through hybrid quantum-classical architectures.We propose a novel hybrid architecture integrating variational quantum circuits (VQC) for feature extraction with transformer-based temporal modeling, achieving a theoretical parameter reduction of 40-60% over equivalent classical architectures.The framework identifies conditions for quantum advantage: high-dimensional sparse features (d > 100), extreme class imbalance (< 0.5% anomaly rate), and distancebased detection.All performance figures are theoretical projections from the literature; empirical validation is designated as future work.Our comparative analysis reveals that quantum kernel methods show theoretical improvement potential over classical baselines under these conditions.
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.
Financial markets are complex, dynamic systems characterized by nonlinear patterns, stochastic behavior, and high-dimensional data, making accurate stock market prediction a challenging task. Traditional statistical models often fail to capture these complexities, leading to the growing adoption of artificial intelligence and quantum computing techniques. This review presents a comprehensive analysis of optimized equivariant split attention quantum neural network frameworks for stock market prediction and recommendation systems. The approach integrates quantum neural networks with equivariant architectures to preserve structural relationships in financial data, while split attention mechanisms enhance the modeling of both local and global temporal dependencies. The study examines various optimization strategies, including variational quantum circuits, evolutionary algorithms, and hybrid quantum-classical training methods, focusing on their scalability and robustness. It also explores the integration of heterogeneous data sources such as price data, macroeconomic indicators, and sentiment analysis to improve prediction accuracy. Evaluations across diverse financial datasets demonstrate improved forecasting performance, generalization, and computational efficiency compared to conventional models. Overall, the framework offers a promising direction for developing intelligent, scalable, and high-performance financial prediction and recommendation systems.