Financial risk management faces the essential problem of forecasting stock market volatility because traditional econometric models including GARCH fail to demonstrate the complex market behavior which characterizes emerging markets. The research investigates how quantum computing functions as an innovative forecasting method through its assessment of actual volatility patterns observed in four primary Indian stock market indexes between 2015 and 2025. Researchers tested two hybrid quantum-classical systems which included a Variational Quantum Regressor (VQR) and a Quantum Kernel Support Vector Regression (QK-SVR) against traditional GARCH family models and Long Short-Term Memory (LSTM) networks. The results demonstrate that quantum-assisted models achieve better performance than both traditional methods and deep learning benchmarks throughout all market scenarios while maintaining stability during financial emergencies. The VQR model which included India VIX data achieved maximum performance because it decreased Root Mean Square Error (RMSE) by 90% when compared to GARCH and by 65% to 76% when measured against LSTM. The current Noisy Intermediate-Scale Quantum (NISQ) era allows hybrid quantum models to function as efficient and accurate forecasting instruments for emerging market predictions.
Financial risk management faces the essential problem of forecasting stock market volatility because traditional econometric models including GARCH fail to demonstrate the complex market behavior which characterizes emerging markets. The research investigates how quantum computing functions as an innovative forecasting method through its assessment of actual volatility patterns observed in four primary Indian stock market indexes between 2015 and 2025. Researchers tested two hybrid quantum-classical systems which included a Variational Quantum Regressor (VQR) and a Quantum Kernel Support Vector Regression (QK-SVR) against traditional GARCH family models and Long Short-Term Memory (LSTM) networks. The results demonstrate that quantum-assisted models achieve better performance than both traditional methods and deep learning benchmarks throughout all market scenarios while maintaining stability during financial emergencies. The VQR model which included India VIX data achieved maximum performance because it decreased Root Mean Square Error (RMSE) by 90% when compared to GARCH and by 65% to 76% when measured against LSTM. The current Noisy Intermediate-Scale Quantum (NISQ) era allows hybrid quantum models to function as efficient and accurate forecasting instruments for emerging market predictions.
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.
This research explores a novel hybrid neural network architecture for stock price forecasting that integrates Variational Quantum Layers (VQLs) to enhance efficiency and trainability. It examines various quantum-classical algorithms through extensive simulations on both classical and quantum hardware, utilizing key stock indicators like MACD, EMA, Bollinger Bands, and momentum metrics, alongside sentiment analysis of financial news. The dataset used includes real-time stock prices for 15 companies, focusing on Merck & Co. (MRK). By framing stock price forecasting as a binary classification problem, It proposes QLSTM4FM i.e. a unique framework based on quantum-assisted LSTM model, which significantly enhances stock price forecasting. It conducts a thorough and comprehensive performance evaluation of the proposed framework against classical LSTM models. Results show that QLSTM4FM, featuring quantum-enhanced layers, outperforms classical LSTM models in accuracy, lower loss, and faster convergence, demonstrating the potential of quantum computing in financial forecasting.
Financial markets undergo rapid oscillations driven by several volatile causes, necessitating expedited and precise models for risk evaluation and market trend forecasting. Classical models encounter computational constraints when examining high-dimensional, nonlinear datasets. This study seeks to use Quantum Machine Learning (QML) methodologies to expedite risk evaluation and improve volatility forecasting for various financial instruments in real-time settings. The unique contribution of this work is the integration of quantum machine learning with financial risk assessment, leveraging quantum parallelism and kernelized feature spaces to improve the accuracy of market volatility prediction. This framework not only enhances computational efficiency compared to classical models but also provides a scalable foundation for real-time financial decision-making. The system employs Kaggle-style datasets including CME futures, Microsoft (MSFT), Amazon (AMZN), HRB, and historical data from the Dow Jones (DJI). Essential metrics including Open, Close, High, Low, Volume, Volatility Index, and Price Returns were retrieved. A hybrid pipeline combining conventional preprocessing with quantum-enhanced classifiers, including variational quantum circuits, was developed using the Qiskit and Pennylane frameworks. The QML-based model had a prediction accuracy of 95.2% in risk categorization and market direction forecasting, exceeding traditional standards. Quantum models provide significant benefits in velocity and scalability for financial analytics, enabling real-time risk assessment and strategic investment analysis.
The stock markets have become a popular topic within machine learning (ML) communities, with one particular application being stock price prediction. However, accurately predicting the stock market is a challenging task due to the various factors within financial markets. With the introduction of ML, prediction techniques have become more efficient but computationally demanding for classical computers. Given the rise of quantum computing (QC), which holds great promise for being exponentially faster than current classical computers, it is natural to explore ML within the QC domain. In this study, we leverage a hybrid quantum-classical ML approach to predict a company's stock price. We integrate classical long short-term memory (LSTM) with QC, resulting in a new variant called QLSTM. We initially validate the proposed QLSTM model by leveraging an IBM quantum simulator running on a classical computer, after which we conduct predictions using an IBM real quantum computer. Thereafter, we evaluate the performance of our model using the root mean square error (RMSE) and prediction accuracy. Additionally, we perform a comparative analysis, evaluating the prediction performance of the QLSTM model against several other classical models. Further, we explore the impacts of hyperparameters on the QLSTM model to determine the best configuration. Our experimental results demonstrate that while the classical LSTM model achieved an RMSE of 0.0693 and a prediction accuracy of 0.8815, the QLSTM model exhibited superior performance, achieving values of 0.0602 and 0.9736, respectively. Furthermore, the QLSTM outperformed other classical models in both metrics.
Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data. Recently, quantum computing has emerged as a promising paradigm for solving complex high-dimensional sampling problems. To harness this, we propose a novel hybrid framework combining the temporal representation power of classical neural networks with the distribution-learning capabilities of quantum models. Specifically, we integrate a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM). The LSTM extracts dynamic features, while the QCBM acts as a learnable generative prior modeling complex market distributions to guide forecasting. Evaluated on 5-minute high-frequency data from the SSE Composite and CSI 300 indices, our model significantly outperforms a classical LSTM baseline across MSE, RMSE, and QLIKE metrics. Furthermore, by introducing a stochastic ``Drop-Prior" mechanism during training, the LSTM implicitly distills structured information from the quantum prior. This establishes a pragmatic paradigm of ``quantum-assisted training with classical-efficient inference", whereby the model retains its quantum-enhanced accuracy even when the quantum module is entirely disabled during deployment. This demonstrates a practical pathway for leveraging quantum computing to enhance classical models without real-time quantum inference latency.
Stock market forecasting remains one of the most computationally demanding tasks in financial engineering, requiring sophisticated models capable of processing nonlinear patterns, high-frequency volatility, and large volumes of time-series data. Recent advancements in quantum computing offer the potential to surpass classical machine learning (ML) by leveraging principles such as superposition and entanglement to accelerate learning and optimization. This paper conducts a comprehensive comparison between quantum machine learning (QML) models and classical deep learning architectures for stock price prediction, emphasizing the technological improvements and optimization of computational methods. Our analysis integrates insights from prior studies, including the application of quantum-enhanced Gramian Angular Fields with CNNs [2], shallow parameterized quantum circuits for market indicators [4], and hybrid quantum-classical algorithms tailored to finance [1]. Experimental evaluation (using IBM Qiskit's quantum simulators and real hardware) demonstrates competitive performance of quantum neural networks (QNNs) against state-of-the-art recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures [3]. Across AAPL/MSFT and short horizons, compact QNNs match strong LSTM baselines while using an order of magnitude fewer parameters, and—critically—transfer from calibrated simulators to IBM hardware with marginal degradation after mitigation. This device-aware training/evaluation loop exemplifies a deployable path for financial QML under realistic noise and resource constraints and motivates standardized, time-series-aware testing for the community. Results highlight the engineering value of quantum acceleration in feature encoding, the research novelty of hybrid ensemble strategies, and the future scope of benchmarking across leading quantum computing machines such as IBM, Google Sycamore, Rigetti, IonQ, and Xanadu.