Quantum Machine Learning for High-Frequency Trading and Risk Management

Rana Veer Samara Sihman Bharattej Rupavath; Omkar Reddy Polu; Balaiah Chamarthi; Tanay Chowdhury; Pratik Kasralikar; Sandipkumar Patel; Ramkrishna Tumati; Abdul Aleem Syed; Nuzhat Prova

doi:10.2991/978-94-6463-872-1_42

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Quantum Machine Learning for High-Frequency Trading and Risk Management

Authors

Rana Veer Samara Sihman Bharattej Rupavath¹, Omkar Reddy Polu², Balaiah Chamarthi³, Tanay Chowdhury⁴, Pratik Kasralikar⁵, Sandipkumar Patel⁶, Ramkrishna Tumati⁷, Abdul Aleem Syed⁸, Nuzhat Prova⁹^{, *}

¹Dept. of Business Administration, National Louis University, Tampa, FL, USA

²Dept. of Technology and Innovation, City National Bank, Los Angeles, CA, USA

³Dept. of Technology & Innovation, Info Services LLC, Livonia, MI, USA

⁴Data Science, AWS Gen AI Innovation Center, Seattle, WA, USA

⁵Dept. of Business Administration, Lindsey Wilson College, Columbia, KY, USA

⁶Independent Researcher, Ex-Gujarat Technological University, Ahmedabad, India

⁷Dept. of Software Application & Engineering, Intel, Aloha, OR, USA

⁸SVP Technical Product Management, FHN Financial, Memphi, TN, USA

⁹Independent Researcher, New york, NY, USA

^*Corresponding author. Email: nuzhatnsu@gmail.com

Corresponding Author

Nuzhat Prova

Available Online 4 November 2025.

DOI: 10.2991/978-94-6463-872-1_42 How to use a DOI?
Keywords: Quantum Support Vector Machines (QSVM); Variational Quantum Classifiers (VQC); High-frequency trading (HFT); QML; Risk Management
Abstract: 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 learn- ing 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 standardmethods achieving 92.7% success rate. Using quantum circuits to sort finan- cial data the VQC model reached 91.3% accuracy which proves its potential for advanced risk analysis and business forecasting applications. Quantum machine learning models show they can solve HFT issues faster and with better results than traditional methods in this study. Quantum computing offers strong mar- ket potential by processing bigger data sets and making faster precise financial forecasts. Future studies will study how to upgrade quantum algorithms for HFT systems to produce enhanced trading outcomes through speedier decision-making and greater strategy optimization while lowering operational risks.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)
Series: Advances in Economics, Business and Management Research
Publication Date: 4 November 2025
ISBN: 978-94-6463-872-1
ISSN: 2352-5428
DOI: 10.2991/978-94-6463-872-1_42 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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ris enw bib

TY  - CONF
AU  - Rana Veer Samara Sihman Bharattej Rupavath
AU  - Omkar Reddy Polu
AU  - Balaiah Chamarthi
AU  - Tanay Chowdhury
AU  - Pratik Kasralikar
AU  - Sandipkumar Patel
AU  - Ramkrishna Tumati
AU  - Abdul Aleem Syed
AU  - Nuzhat Prova
PY  - 2025
DA  - 2025/11/04
TI  - Quantum Machine Learning for High-Frequency Trading and Risk Management
BT  - Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)
PB  - Atlantis Press
SP  - 668
EP  - 680
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-872-1_42
DO  - 10.2991/978-94-6463-872-1_42
ID  - Rupavath2025
ER  -

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