Quantum Computing in High Frequency Trading and Fraud Detection

Authors

  • Apoorva Ganapathy Adobe Systems

DOI:

https://doi.org/10.18034/ei.v9i2.549

Keywords:

Quantum Computing, Qubits, Fraud Detection, Atom, Entanglement

Abstract

‘Quantum Computing in high-frequency trading and fraud detection is an analysis of quantum computing and how it can be used by the different industries especially finance. It is an evolution of computing from the traditional computing method. Quantum computing is a process that is concentrated on creating systems and technology based on quantum theory rules. Quantum theory describes the energy on atomic and subatomic levels. Quantum computing uses quantum bits (qubits) which are more advanced than the traditional bits used by traditional computers. This article focuses on deploying quantum computers in solving problems that cannot be efficiently solved using traditional computers. In the finance sector, such as banking, insurance, and high-frequency trading, quantum computers can help optimize service by providing targeting and predictive analytics to reduce risk, provide personalized customer service, and provide the needed security framework against fraud.

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Author Biography

Apoorva Ganapathy, Adobe Systems

Senior Developer, Adobe Systems, San Jose, California, USA

References

Ahmed, A. A. A., Aljarbouh, A., Donepudi, P. K., & Choi, M. S. (2021a). Detecting Fake News using Machine Learning: A Systematic Literature Review. Psychology and Education, 58(1), 1932–1939. https://zenodo.org/record/4494366

Ahmed, A. A. A., Donepudi, P. K., & Asadullah, A. B. M. (2020). Artificial Intelligence in Clinical Genomics and Healthcare. European Journal of Molecular & Clinical Medicine, 7(11), 1194-1202, https://ejmcm.com/?_action=article&au=24014

Ahmed, A. A. A., Paruchuri, H., Vadlamudi, S., & Ganapathy, A. (2021b). Cryptography in Financial Markets: Potential Channels for Future Financial Stability. Academy of Accounting and Financial Studies Journal, 25(4), 1–9. https://doi.org/10.5281/zenodo.4774829

Asadullah, A., Juhdi, N. B., Islam, M. N., Ahmed, A. A. A., & Abdullah, A. (2019). The Effect of Reinforcement and Punishment on Employee Performance. ABC Journal of Advanced Research, 8(2), 47-58. https://doi.org/10.18034/abcjar.v8i2.87

Azad, M. M., Ganapathy, A., Vadlamudi, S., Paruchuri, H. (2021). Medical Diagnosis using Deep Learning Techniques: A Research Survey. Annals of the Romanian Society for Cell Biology, 25(6), 5591–5600. Retrieved from https://www.annalsofrscb.ro/index.php/journal/article/view/6577

Doewes, R. I.; Ahmed, A. A. A.; Bhagat, A.; Nair, R.; Donepudi, P. K.; Goon, S.; Jain, V.; Gupta, S.; Rathore, N. K.; Jain, N. K. (2021). A regression analysis based system for sentiment analysis and a method thereof. Australian Official Journal of Patents, 35(17), Patent number: 2021101792. https://lnkd.in/gwsbbXa

Donepudi, P. K., Ahmed, A. A. A., Hossain, M. A., & Maria, P. (2020a). Perceptions of RAIA Introduction by Employees on Employability and Work Satisfaction in the Modern Agriculture Sector. International Journal of Modern Agriculture, 9(4), 486–497. https://doi.org/10.5281/zenodo.4428205

Donepudi, P. K., Ahmed, A. A. A., Saha, S. (2020b). Emerging Market Economy (EME) and Artificial Intelligence (AI): Consequences for the Future of Jobs. Palarch’s Journal of Archaeology of Egypt/Egyptology, 17(6), 5562-5574. https://archives.palarch.nl/index.php/jae/article/view/1829

Donepudi, P. K., Banu, M. H., Khan, W., Neogy, T. K., Asadullah, ABM., & Ahmed, A. A. A. (2020c). Artificial Intelligence and Machine Learning in Treasury Management: A Systematic Literature Review. International Journal of Management, 11(11), 13–22. https://doi.org/10.5281/zenodo.4247297

Ganapathy, A., & Neogy, T. K. (2017). Artificial Intelligence Price Emulator: A Study on Cryptocurrency. Global Disclosure of Economics and Business, 6(2), 115-122. https://doi.org/10.18034/gdeb.v6i2.558

Ganapathy, A., Redwanuzzaman, M., Rahaman, M. M., & Khan, W. (2020). Artificial Intelligence Driven Crypto Currencies. Global Disclosure of Economics and Business, 9(2), 107-118. https://doi.org/10.18034/gdeb.v9i2.557

Neogy, T. K., & Paruchuri, H. (2014). Machine Learning as a New Search Engine Interface: An Overview. Engineering International, 2(2), 103-112. https://doi.org/10.18034/ei.v2i2.539

Paruchuri, H. (2015). Application of Artificial Neural Network to ANPR: An Overview. ABC Journal of Advanced Research, 4(2), 143-152. https://doi.org/10.18034/abcjar.v4i2.549

Paruchuri, H. (2017). Credit Card Fraud Detection using Machine Learning: A Systematic Literature Review. ABC Journal of Advanced Research, 6(2), 113-120. https://doi.org/10.18034/abcjar.v6i2.547

Paruchuri, H. (2019). Market Segmentation, Targeting, and Positioning Using Machine Learning. Asian Journal of Applied Science and Engineering, 8(1), 7-14. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1193

Rahman, M. M., Chowdhury, M. R. H. K., Islam, M. A., Tohfa, M. U., Kader, M. A. L., Ahmed, A. A. A., & Donepudi, P. K. (2020). Relationship between Socio-Demographic Characteristics and Job Satisfaction: Evidence from Private Bank Employees. American Journal of Trade and Policy, 7(2), 65-72. https://doi.org/10.18034/ajtp.v7i2.492

Vadlamudi, S. (2016). What Impact does Internet of Things have on Project Management in Project based Firms?. Asian Business Review, 6(3), 179-186. https://doi.org/10.18034/abr.v6i3.520

Vadlamudi, S. (2018). Agri-Food System and Artificial Intelligence: Reconsidering Imperishability. Asian Journal of Applied Science and Engineering, 7(1), 33-42. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1192

Vadlamudi, S. (2019). How Artificial Intelligence Improves Agricultural Productivity and Sustainability: A Global Thematic Analysis. Asia Pacific Journal of Energy and Environment, 6(2), 91-100. https://doi.org/10.18034/apjee.v6i2.542

Verma, B. K.; Lokulwar, P.; Aquatar, M. O.; Panda, R. B.; Raghuwanshi, G. K.; Dixit, P.; Nigam, U.; Khan, I. R.; Kumar, P.; Ahmed, A. A. A. (2021). A SMART CITY SYSTEM FOR CITIZEN'S UTILIZING UBIQUITOUS COMPUTING TECHNIQUE. Australian Official Journal of Patents, 35(12), Page No. 1873, Patent number: 2021101194. https://lnkd.in/gw6A3Nd

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Published

2021-07-01

How to Cite

Ganapathy, A. (2021). Quantum Computing in High Frequency Trading and Fraud Detection. Engineering International, 9(2), 61–72. https://doi.org/10.18034/ei.v9i2.549

Issue

Section

Peer Reviewed Articles