This paper presents a hybrid framework that integrates a classical multi-agent system with a Quantum Long Short-Term Memory (QLSTM) network to predict stock market sentiment from agent-generated news articles. Using a team of AI agents powered by Ollama and Llama 3.2, simulated stock market news is dynamically generated and vectorized using TF-IDF text processing. The resulting sequence embeddings are fed into a 4-qubit simulated quantum environment—built with PennyLane and PyTorch—where quantum circuit gates leverage superposition and entanglement to drive LSTM sequence learning. The combined quantum-classical architecture outputs binary sentiment predictions (bullish vs. bearish) for complex financial text analysis. The work demonstrates the architectural viability of bridging multi-agent data generation with quantum-enhanced deep learning for financial modeling. Original publication: Published in "Python in Plain English" on Medium (July 3, 2025). Direct link : https://python.plainenglish.io/quantum-enhanced-ai-for-stock-market-prediction-a-multi-agent-approach-with-qlstm-afe16d1687af
This paper presents a hybrid framework that integrates a classical multi-agent system with a Quantum Long Short-Term Memory (QLSTM) network to predict stock market sentiment from agent-generated news articles. Using a team of AI agents powered by Ollama and Llama 3.2, simulated stock market news is dynamically generated and vectorized using TF-IDF text processing. The resulting sequence embeddings are fed into a 4-qubit simulated quantum environment—built with PennyLane and PyTorch—where quantum circuit gates leverage superposition and entanglement to drive LSTM sequence learning. The combined quantum-classical architecture outputs binary sentiment predictions (bullish vs. bearish) for complex financial text analysis. The work demonstrates the architectural viability of bridging multi-agent data generation with quantum-enhanced deep learning for financial modeling. Original publication: Published in "Python in Plain English" on Medium (July 3, 2025). Direct link : https://python.plainenglish.io/quantum-enhanced-ai-for-stock-market-prediction-a-multi-agent-approach-with-qlstm-afe16d1687af
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.
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.
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.
Predicting the stock market has been an uphill battle with the non-linear, intricate, and highly unpredictable nature of the market. Classic machine learning and deep learning techniques have performed but lack scalability, computational cost, and nuance in capturing market patterns. Quantum computing (QC) has been a game-changing instrument for dealing with massive financial data and tuning predictive models for the past several years. Here, we are demonstrating the application of quantum machine learning (QML) methods, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum Boltzmann machines (QBM), to stock market prediction. Employing superposition, entanglement, and parallelism in quantum computation, we will improve prediction precision, improve feature optimisation, and strengthen trend analysis of the market. Our approach utilises quantum-enhanced financial modelling to handle large volumes of historical stock data and sentiment analysis within a high-dimensional Hilbert space. The findings show that quantum models surpass classical models in capturing market trends, computation time reduction, and managing intricate correlations. Although quantum hardware is in its nascent stage, this research proves the promise of quantum computing as a disruptor in financial forecasting.
Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being projected as the first fields that will be impacted by the rise of quantum machines. Quantum computers are being used today in drug discovery, material & molecular modelling and finance. In this work, we explore emerging research areas in the application of hybrid classical quantum machine learning (QML) in finance. We focus on hybrid classical-quantum models that have gained significant interest in the financial world for various applications. We utilise a real-world cryptocurrency dataset to train a quantum generative adversarial networks (qGAN), and implement the real-life NVIDIA stock price data on quantum LSTM (QLSTM), comparing its performance with classical Long Short Term Memory (LSTM) model. Additionally, we investigate the application of quantum circuit Born machine (QCBM) using a toy dataset for applications in finance. For the qGAN, we define quantum circuits for generators and showcase the potential for future quantum advantage in finance through QML, possibly in combination with QLSTM and QCBM. Our findings suggest that, while training classical data is more efficient on LSTM, the test predictions of QLSTM is slightly superior to its classical counterpart making it a promising candidate for financial forecasting and analysis for timeseries type data.
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.