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.
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.
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.
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.
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.
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.
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.
The need for smart, scalable algorithms to predict stock market movements has grown in response to the deluge of financial data coming from disparate sources including news articles, social media, and market pricing. Despite its success, traditional deep learning is ill-equipped to handle the complex nonlinear relationships and high-dimensional interactions found in financial markets. To get around these problems, this study presents a paradigm for quantum-enhanced multimodal intelligence that uses deep learning and quantum computing to predict stock market trends. Through the use of quantum-inspired feature mapping and quantumassisted optimisation, modality-specific encoders create representations from numerical market indicators and textual sentiment data. Both representation learning and exploring solution spaces are improved by this hybrid design. When tested on industry-standard financial datasets, the suggested model achieved better accuracy, precision, recall, and F1-score than typical multimodal deep learning baselines. The results of ablation studies further demonstrate that combining multimodal fusion with quantum enhancement improves the accuracy of predictions. Based on the findings, next-gen financial forecasting systems may incorporate quantum-augmented intelligence.