International Research Journal on Advanced Science Hub

Quantum Machine Learning Approaches for Real-Time Market Pattern Recognition in High-Frequency Trading: A Banking Sector Application

Hari Krishn Gupta, Naveen Kumar Vayyasi, Jagadeesh Thiruveedula

Published Jun 27, 2025• 0 citations

Abstract

Recognizing market trends in HFT gets simpler and faster thanks to quantum machine learning (QML) which also has the power to exceed classical model boundaries. Using quantum kernel methods and variational quantum circuits, QML can analyze high-dimensional financial information such as tick-by-tick data and order book samples, in the superposition state simultaneously. Comparative work proves that quantum-enhanced models have better accuracy, can detect patterns better and perform faster decisions on near-term quantum devices than classical ones. To be useful in banking-sector HFT, QML needs to easily connect with all existing internal information flows and trades. Essentially, users need to address hardware noise, guard against decoherence on NISQ machines, balance circuit complexity with tight time goals and guarantee scalable training for a quantum-classical mix. Latest progress in handling errors, such as zero-noise extrapolation, and adapting features has shown that models become stronger under realistic kinds of noise, although greater development is necessary to reach a production-ready level of reliability. Here, we bring together the most current work in QML for HFT, including different approaches, both software and hardware and ways to add quantum components to classical optimization loops. Outcomes from benchmarking are discussed, as well as the differing designs in kernel and variational approaches and significant parts of regulations related to quantum-assisted trading. In conclusion, a plan for more detailed studies is given, underlining how important consistent performance measures, detachable frameworks that can be used together and cooperation between technical fields are to get QML from research trials to compliant solutions in high-speed stock markets.

DOI: 10.47392//irjash.2025.072

Related papers

QML Algorithm for Market Pattern Detection in High-Frequency Trading for Banking

Hari Gupta, Deepesh Vinodkumar Semlani, Naveen Kumar Vayyasi +3 more

HFT is also referred to as high-frequency trading, is a trading model that is used in the financial markets. Market conditions are analysed by computer algorithms and buy and sell actions made in milliseconds to profit. It will fail through huge amounts of statistics and the intricate market trends that HFT analytics will work with, the investors will inadvertently and swiftly understand market trends. That leads to missed business opportunities and missed capacities. One way to solve this challenge is a QML algorithm designed on DSLs in HFT in banking which uses quantum device mastering techniques at the lowest implementation level. In terms of quantum computing principles, this algorithm is thought to integrate the classical machine-mastering techniques of 1 st principles with the quantum computing requirements of more effective and faster pattern-finding in HFT. At the heart of the algorithm is quantum entanglement, which creates a graph of information nodes related to each other dynamically. This allows the guideline set to quickly determine and analyze the elaborate designs on huge databases appropriately, tenting the buyers with the readout expander, the finest move near marketplace. The following features make the QML algorithm a powerful instrument in the buying and selling of the fast-changing and volatile environment. Its speed and accuracy should provide the buyers with a vicious advantage to be able to make well-informed and profitable trading decisions. QML algorithm marketplace sample Detection in HFT banking offers a solution to banks and financial institutions that want to streamline their HFT strategies and gain more income.

Aug 11, 20250 citations

Quantum Machine Learning for High-Frequency Trading and Risk Management

Rana Veer Samara Sihman Brahmatej Rupavath, Omkar Reddy Polu, Balaiah Chamarthi +6 more

High-Frequency Trading(HFT) needs computers to run trading operations over thousands of transactions every microsecond.The resulting huge data volumes must process fast with reliable predictions.Because traditional machine learning systems cannot process this complex high-dimensional information they need better solutions.We test the capacity of QSVM and VQC algorithms to boost prediction accuracy and strengthen HFT risk management systems.Our research team used QML methods to process trading data from a Kaggle simulation to refine predictive outcomes and make decisions faster.Our tests show that QSVM with quantum kernel processing handles large datasets better than standard

Advances in economics, business and management research/Advances in Economics, Business and Management ResearchJan 1, 20250 citations

Advancing Stock Price Prediction and Algorithmic Trading: Quantum Machine Learning for High-Frequency Optimization

Sharmila C, Sivaraju S.S, Anuradha .T +3 more

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.

International Research Journal of Multidisciplinary TechnovationJan 30, 20260 citations

Quantum Machine Learning Architectures for Stock Market Forecasting, Anomaly Detection, and Pattern Recognition in Financial Time Series

Tugan Başaran

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.

Next generation.Feb 7, 20260 citations

ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING

Ananya (Ph.D. Candidate) Patel

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.

International Journal of Intelligent Data and Machine LearningFeb 1, 20252 citations

Supervised Quantum Support Vector Algorithms for High Frequency Financial Forecasting

Dr.E Mohanraj, K Anitha, Dr.S. Karunakaran +2 more

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.

International Journal of Artificial Intelligence and Machine LearningMay 24, 20260 citations