This review explores the emerging field of quantum-classical hybrid approaches for predicting stock price movement. Predicting the stock price movement has been a popular topic amongst machine learning (ML) enthusiasts. However, predicting how a particular stock or stock market will perform is a difficult task, due to market dynamics, company (stock) specific dynamics, volatility, and other environmental factors. Using advanced classic machine learning techniques, such predictions have become possible, but also computationally complex. There is a need to develop a non-linear prediction model for predicting the movement of the price of a stock, in a more accurate and faster manner. We examine recent advancements in quantum computing algorithms, focusing on the potential to enhance the efficiency and accuracy of traditional prediction models. This review delves into key concepts such as quantum feature maps, variational quantum circuits, and hybrid architectures that integrate quantum and classical components. We discuss the potential advantages of quantum-enhanced techniques, for their ability to process complex financial data efficiently and unlock hidden patterns. Furthermore, we analyze the current challenges and limitations. This review aims to provide an in-depth overview of the latest quantum-classical stock price prediction, highlighting promising avenues for further research and development in this exciting field.
This review explores the emerging field of quantum-classical hybrid approaches for predicting stock price movement. Predicting the stock price movement has been a popular topic amongst machine learning (ML) enthusiasts. However, predicting how a particular stock or stock market will perform is a difficult task, due to market dynamics, company (stock) specific dynamics, volatility, and other environmental factors. Using advanced classic machine learning techniques, such predictions have become possible, but also computationally complex. There is a need to develop a non-linear prediction model for predicting the movement of the price of a stock, in a more accurate and faster manner. We examine recent advancements in quantum computing algorithms, focusing on the potential to enhance the efficiency and accuracy of traditional prediction models. This review delves into key concepts such as quantum feature maps, variational quantum circuits, and hybrid architectures that integrate quantum and classical components. We discuss the potential advantages of quantum-enhanced techniques, for their ability to process complex financial data efficiently and unlock hidden patterns. Furthermore, we analyze the current challenges and limitations. This review aims to provide an in-depth overview of the latest quantum-classical stock price prediction, highlighting promising avenues for further research and development in this exciting field.
This review explores the emerging field of quantum-classical hybrid approaches for predicting stock price movement. Predicting the stock price movement has been a popular topic amongst machine learning (ML) enthusiasts. However, predicting how a particular stock or stock market will perform is a difficult task, due to market dynamics, company (stock) specific dynamics, volatility, and other environmental factors. Using advanced classic machine learning techniques, such predictions have become possible, but also computationally complex. There is a need to develop a non-linear prediction model for predicting the movement of the price of a stock, in a more accurate and faster manner. We examine recent advancements in quantum computing algorithms, focusing on the potential to enhance the efficiency and accuracy of traditional prediction models. This review delves into key concepts such as quantum feature maps, variational quantum circuits, and hybrid architectures that integrate quantum and classical components. We discuss the potential advantages of quantum-enhanced techniques, for their ability to process complex financial data efficiently and unlock hidden patterns. Furthermore, we analyze the current challenges and limitations. This review aims to provide an in-depth overview of the latest quantum-classical stock price prediction, highlighting promising avenues for further research and development in this exciting field.
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.
Quantum Machine Learning (QML) prediction of stock values is on the verge of changing the financial market and particularly enhancing HFT techniques. This is an attempt at quantum computing-machine learning hybrid aimed at enhancing the accuracy and efficiency of trading decisions. The historical machine learning-based stock price prediction models are incapable of processing large volumes of data, executing trades faster, and modeling complex market dynamics in real-time. The challenges result in ineffective trading decisions and trade lags at high frequencies. These problems are solved by HFT Optimization and Quantum Machine Learning. Because quantum computing capability is processing large amounts of data simultaneously, the proposed architecture enhances prediction accuracy and speed while reducing latency of decision-making. Quantum Neural Networks (QNNs) and quantum-optimized algorithms are useful to boost the modeling of market behavior. This technology will enhance stock price forecasting and optimization of trading strategies, which increases profit and minimize risk. Initial evidence indicates that quantum-based HFT systems are faster in execution speed and market flexibility than conventional techniques, which is essential to the future of automated trading.
We explored the potential applications of various Quantum Algorithms for stock price prediction by conducting a series of experimental simulations using both Classical as well as Quantum Hardware. Firstly, we extracted various stock price indicators, such as Moving Averages (MA), Average True Range (ATR), and Aroon, to gain insights into market trends and stock price movements. Next, we employed Quantum Annealing (QA) for feature selection and Principal Component Analysis (PCA) for dimensionality reduction. Further, we transformed the stock price prediction task essentially into a classification problem. We trained the Quantum Support Vector Machine (QSVM) to predict price movements (whether up or down) contrasted their performance with classical models and analyzed their accuracy on a dataset formulated using Quantum Annealing and PCA individually. We focused on the stock price prediction and binary classification of stock prices for four different companies, namely Apple, Visa, Johnson and Jonson, and Honeywell. We primarily used the real-time stock data of the raw stock prices of these companies. We compared various Quantum Computing techniques with their classical counterparts in terms of accuracy and F-score of the prediction model. Through these experimental simulations, we shed light on the potential advantages and limitations of Quantum Algorithms in stock price prediction and contribute to the growing body of knowledge at the intersection of Quantum Computing and Finance.
We explored the potential applications of various Quantum Algorithms for stock price prediction by conducting a series of experimental simulations using both Classical as well as Quantum Hardware. Firstly, we extracted various stock price indicators, such as Moving Averages (MA), Average True Range (ATR), and Aroon, to gain insights into market trends and stock price movements. Next, we employed Quantum Annealing (QA) for feature selection and Principal Component Analysis (PCA) for dimensionality reduction. Further, we transformed the stock price prediction task essentially into a classification problem. We trained the Quantum Support Vector Machine (QSVM) to predict price movements (whether up or down) and contrasted its performance with classical models and analysed their accuracy on dataset formulated using Quantum Annealing and PCA individually. We focused on stock price prediction and binary classification of stock prices for four different companies, namely Apple, Visa, Johnson and Jonson and Honeywell. We primarily used the real-time stock data of the raw stock prices of these companies. We compared various Quantum Computing techniques with their classical counterparts in terms of accuracy and F-score of the prediction model. Through these experimental simulations, we shed light on the potential advantages and limitations of Quantum Algorithms in stock price prediction and contribute to the growing body of knowledge at the intersection of Quantum Computing and Finance.
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.