Quantum-stimulated AI for Continuous Credit Risk Categorization in High-Frequency Trading
DOI:
https://doi.org/10.46243/jst.2025.v10.i05.pp22-28Keywords:
AI, Credit Risk, Quantum-stimulated AI,, high-frequency trading (HFT),, real-time, computational complications, data latency,, Quantum Approximate Optimization Algorithms (QAOA),, model interpretability, data, speed, accuracyAbstract
This research focuses on the use of quantum-stimulated artificial intelligence approaches for enhancing the credit risk
classification in high-frequency trading systems. This paper considers the several obstacles, the speeds, the accuracy,
and scopes of the improvement of credit risk evaluation by quantum algorithms. The study represents fresh approaches
introduced in trading structures regarding the application of quantum-inspired AI and offers guidelines based on the
best practices of efficient, fast, and flexible credit risk handling in fluctuating markets.
References
Saxena, A., Mancilla, J., Montalban, I. and Pere, C., 2023. Financial Modeling Using Quantum Computing: Design and manage quantum machine learning solutions for financial analysis and decision-making. Packt Publishing Ltd
George, J.G., 2023. Advancing Enterprise Architecture for Post-Merger Financial Systems Integration in Capital Markets Laying the Foundation for Machine Learning Application. Aus. J. ML Res. & App, 3(2), p.429
How, M.L. and Cheah, S.M., 2023. Business Renaissance: Opportunities and challenges at the dawn of the Quantum Computing Era. Businesses, 3(4), pp.585-605
Wilkens, S. and Moorhouse, J., 2023. Quantum computing for financial risk measurement. Quantum Information Processing, 22(1), p.51
Khurana, R.A.H.U.L., 2022. Applications of quantum computing in telecom e-commerce: Analysis of qkd, qaoa, and qml for data encryption, speed optimization, and ai-driven customer experience. Quarterly Journal of Emerging Technologies and Innovations, 7(9), pp.1-15
Mannone, M., Seidita, V. and Chella, A., 2023. Modeling and designing a robotic swarm: A quantum computing approach. Swarm and Evolutionary Computation, 79, p.101297
Mazhar, T., Irfan, H.M., Haq, I., Ullah, I., Ashraf, M., Shloul, T.A., Ghadi, Y.Y., Imran and Elkamchouchi, D.H., 2023. Analysis of challenges and solutions of IoT in smart grids using AI and machine learning techniques: A review. Electronics, 12(1), p.242
Arifovic, J., He, X.Z. and Wei, L., 2022. Machine learning and speed in high-frequency trading. Journal of economic dynamics and control, 139, p.104438
Jani, Y., 2023. Ai-driven risk management and fraud detection in high-frequency trading environments. International Journal of Science and Research (IJSR), 12(11), pp.2223-2229
Mhlanga, D., 2021. Financial inclusion in emerging economies: The application of machine learning and artificial intelligence in credit risk assessment. International journal of financial studies, 9(3), p.39. Abhishek Murikipudi: Quantum-stimulated AI for Continuous Credit Risk Categorization in High-Frequency Trading
Wilkens, S. and Moorhouse, J., 2023. Quantum computing for financial risk measurement. Quantum Information Processing, 22(1), p.51
Saxena, A., Mancilla, J., Montalban, I. and Pere, C., 2023. Financial Modeling Using Quantum Computing: Design and manage quantum machine learning solutions for financial analysis and decision making. Packt Publishing Ltd
Sarkar, S., 2023. Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration. arXiv preprint arXiv:2311.10718
Tang, Y., 2022, December. Tiansuan Constellation: Intelligent Software-Defined Microsatellite with Orbital Attention for Sustainable Development Goals. In International Conference on Big Data Intelligence and Computing (pp. 3-21). Singapore: Springer Nature Singapore
Herman, D., Googin, C., Liu, X., Sun, Y., Galda, A., Safro, I., Pistoia, M. and Alexeev, Y., 2023. Quantum computing for finance. Nature Reviews Physics, 5(8), pp.450-465
Mahesh, V. and Shijo, S., 2023. Accelerating drug discovery with quantum computing. Evolution and Applications of Quantum Computing, pp.175-181
Kumar, P., 2023, April. Deep reinforcement learning for high-frequency market making. In Asian Conference on Machine Learning (pp. 531-546). PMLR
Yoo, S., Kim, H., Kim, J., Park, S., Kim, J.Y. and Oh, J., 2023, February. LightTrader: A standalone high-frequency trading system with deep learning inference accelerators and proactive scheduler. In 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA) (pp. 1017-1030). IEEE
Herman, D., Googin, C., Liu, X., Galda, A., Safro, I., Sun, Y., Pistoia, M. and Alexeev, Y., 2022. A survey of quantum computing for finance. arXiv preprint arXiv:2201.02773
Victoria, G., 2023. Quantum AI for Financial Risk Mitigation: The Next Frontier in Predictive Analytics
