Quantum Computing Improves Efficiency and Productivity in Financial Institutions


  • Jabin Geevarghese George, Manoj Kumar Vandanapu


Financial Management and Any Other dynamically changing variables like phenomena of quantum computing and risk assessment.


Various ways in which quantum computing may transform the financial industry are also explored in this research, and this includes its potential to deal with the problems that we are currently faced with and the opportunities that it may bring. The research starts with a general survey of the existing situation in the area of quantum computing technology which contains lowering of costs and increase in effectiveness. It proposes how quantum computing can vastly change the current performance of banking regarding fraud detection, improvements in risk management measures, and better optimization of various financial process. Additionally, the report reviews the barriers and possible risks that exist in the banking industry implementing quantum computing techniques and, thus, offers crucial points for the successful mitigation of these risks. This study’s outcomes are expected to provide banks with helpful information on how they can exploit the capacities of quantum chips to reinforce their data security strategies and assert themselves against their competitors. The research results present the findings that are practical for the strategic application of quantum computing in the banking sector, identifying an opportunity for the transformative effects of the technology to start changing the expensive and inefficient banking processes and giving rise to a safe and efficient financial environment.


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R. Orús, S. Mugel, and E. Lizaso, “Quantum computing for finance: Overview and prospects,” Reviews in Physics, vol. 4, p. 100028, 2019.

S. Bravyi, D. Gosset, and R. König, “Quantum advantage with shallow circuits,” Science, vol. 362, no. 6412, pp. 308–311, 2018.

M. A. Nielsen and I. L. Chuang, “Quantum computation and quantum information,” Phys. Today, vol. 54, no. 2, p. 60, 2001.

D. J. Egger, C. Gambella, J. Marecek, S. McFaddin, M. Mevissen, R. Raymond, A. Simonetto, S. Woerner, and E. Yndurain, “Quantum computing for finance: State-of-the-art and future prospects,” IEEE Transactions on Quantum Engineering, vol. 1, pp. 1–24, 2020.

N. Vidhya, V. Seethalakshmi, and S. Suganyadevi, “Non-silicon computing with quantum superposition entanglement using qubits,” in Quantum Computing: A Shift from Bits to Qubits. Springer, 2023, pp. 131– 150.

C. M. Dawson, H. L. Haselgrove, A. P. Hines, D. Mortimer, M. A. Nielsen, and T. J. Osborne, “Quantum computing and polynomial equations over the finite field z_2,” arXiv preprint quant-ph/0408129, 2004.

X.-D. Cai, C. Weedbrook, Z.-E. Su, M.-C. Chen, M. Gu, M.-J. Zhu, L. Li, N.-L. Liu, C.-Y. Lu, and J.-W. Pan, “Experimental quantum computing to solve systems of linear equations,” Physical review letters, vol. 110, no. 23, p. 230501, 2013.

J. Strumpfer and K. Schulten, “Open quantum dynamics calculations with the hierarchy equations of motion on parallel computers,” Journal of chemical theory and computation, vol. 8, no. 8, pp. 2808–2816, 2012.

S. Hallgren and U. Vollmer, “Quantum computing,” in post-quantum cryptography. Springer, 2008, pp. 15– 34.

E. National Academies of Sciences, Medicine et al., “Quantum computing: progress and prospects,” 2019.

S. S. Gill, A. Kumar, H. Singh, M. Singh, K. Kaur, M. Usman, and R. Buyya, “Quantum computing: A taxonomy, systematic review and future directions,” Software: Practice and Experience, vol. 52, no. 1, pp. 66–114, 2022.

O. Covers and M. Doeland, “How the financial sector can anticipate the threats of quantum computing to keep payments safe and secure,” Journal of Payments Strategy & Systems, vol. 14, no. 2, pp. 147–156, 2020.

M. Zhang, L. Xie, Z. Zhang, Q. Yu, G. Xi, H. Zhang, F. Liu, Y. Zheng, Y. Zheng, and S. Zhang, “Exploiting different levels of parallelism in the quantum control microarchitecture for superconducting qubits,” in MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, 2021, pp. 898–911.

M. Marzec, “Portfolio optimization: Applications in quantum computing,” Handbook of High-Frequency Trading and Modeling in Finance, pp. 73–106, 2016.

G. Carrascal, P. Hernamperez, G. Botella, and A. del Barrio, “Backtesting quantum computing algorithms for portfolio optimization,” IEEE Transactions on Quantum Engineering, 2023.

D. J. Egger, R. G. Gutiérrez, J. C. Mestre, and S. Woerner, “Credit risk analysis using quantum computers,” IEEE transactions on computers, vol. 70, no. 12, pp. 2136–2145, 2020.

Kerenidis, A. Prakash, and D. Szilágyi, “Quantum algorithms for portfolio optimization,” in Proceedings of the 1st ACM Conference on Advances in Financial Technologies, 2019, pp. 147–155.

Gunjan and S. Bhattacharyya, “A brief review of portfolio optimization techniques,” Artificial Intelligence Review, vol. 56, no. 5, pp. 3847– 3886, 2023.

E. Grant, T. S. Humble, and B. Stump, “Benchmarking quantum annealing controls with portfolio optimization,” Physical Review Applied, vol. 15, no. 1, p. 014012, 2021. 20) B. Andreas, B. Guillaume, J. Binder, B. Thierry, H. Ehm, T. Ehmer, M. Erdmann, G. Norbert, H. Philipp, M. Hess et al., “Industry quantum computing applications,” EPJ Quantum Technology, vol. 8, no. 1, 2021.

O. Kyriienko and E. B. Magnusson, “Unsupervised quantum machine learning for fraud detection,” arXiv preprint arXiv:2208.01203, 2022.

Di Pierro and M. Incudini, “Quantum machine learning and fraud detection. protocols. strands, and logic,” 2021.

N. Innan, M. A.-Z. Khan, and M. Bennai, “Financial fraud detection: a comparative study of quantum machine learning models,” arXiv preprint arXiv:2308.05237, 2023.

M. Grossi, N. Ibrahim, V. Radescu, R. Loredo, K. Voigt, C. Von Altrock, and A. Rudnik, “Mixed quantum– classical method for fraud detection with quantum feature selection,” IEEE Transactions on Quantum Engineering, vol. 3, pp. 1–12, 2022.

T. Pourhabibi, K.-L. Ong, B. H. Kam, and Y. L. Boo, “Fraud detection: A systematic literature review of graph-based anomaly detection approaches,” Decision Support Systems, vol. 133, p. 113303, 2020.

K. G. Al-Hashedi and P. Magalingam, “Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019,” Computer Science Review, vol. 40, p. 100402, 2021.

Ganapathy, “Quantum computing in high frequency trading and fraud detection,” Engineering International, vol. 9, no. 2, pp. 61–72, 2021.

V. Murinde, E. Rizopoulos, and M. Zachariadis, “The impact of the fintech revolution on the future of banking: Opportunities and risks,” International Review of Financial Analysis, vol. 81, p. 102103, 2022.

J. Deodoro, M. Gorbanyov, M. Malaika, T. S. Sedik, and S. Peiris, “Quantum computing and the financial system: Spooky action at a distance?” IMF Working Papers, vol. 2021, no. 071, 2021.

F. D. Albareti, T. Ankenbrand, D. Bieri, E. Hänggi, D. Lötscher, S. Stettler, and M. Schöngens, “A structured survey of quantum computing for the financial industry,” arXiv preprint arXiv:2204.10026, 2022.

Sharma and S. Lenka, “Authentication in online banking systems through quantum cryptography,” Int. J. Engineering and Technology, vol. 5, pp. 2696–2700, 2013.

F. Bova, A. Goldfarb, and R. G. Melko, “Commercial applications of quantum computing,” EPJ quantum technology, vol. 8, no. 1, p. 2, 2021. 33) N. P. De Leon, K. M. Itoh, D. Kim, K. K. Mehta, T. E. Northup, H. Paik, B. Palmer, N. Samarth, S. Sangtawesin, and D. W. Steuerman, “Materials challenges and opportunities for quantum computing hardware,” Science, vol. 372, no. 6539, p. eabb2823, 2021.

Y.-J. Chang, M.-F. Sie, S.-W. Liao, and C.-R. Chang, “The prospects of quantum computing for quantitative finance and beyond,” IEEE Nanotechnology Magazine, 2023.

M. Fellous-Asiani, J. H. Chai, R. S. Whitney, A. Auffèves, and H. K. Ng, “Limitations in quantum computing from resource constraints,” PRX Quantum, vol. 2, no. 4, p. 040335, 2021.

S. K. Sood et al., “Quantum computing review: A decade of research,” IEEE Transactions on Engineering Management, 2023.

H. Bhatt and S. Gautam, “Quantum computing: A new era of computer science,” in 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2019, pp. 558–561.

Mavroeidis, K. Vishi, M. D. Zych, and A. Jøsang, “The impact of quantum computing on present cryptography,” arXiv preprint arXiv:1804.00200, 2018.

M. Njorbuenwu, B. Swar, and P. Zavarsky, “A survey on the impacts of quantum computers on information security,” in 2019 2nd International conference on data intelligence and security (ICDIS). IEEE, 2019, pp. 212–218.

Naik, E. Yeniaras, G. Hellstern, G. Prasad, and S. K. L. P. Vishwakarma, “From portfolio optimization to quantum blockchain and security: A systematic review of quantum computing in finance,” arXiv preprint arXiv:2307.01155, 2023.

S. Bravyi, O. Dial, J. M. Gambetta, D. Gil, and Z. Nazario, “The future of quantum computing with superconducting qubits,” Journal of Applied Physics, vol. 132, no. 16, 2022.

D. Córcoles, A. Kandala, A. Javadi-Abhari, D. T. McClure, A. W. Cross, K. Temme, P. D. Nation, M. Steffen, and J. M. Gambetta, “Challenges and opportunities of near-term quantum computing systems,” Proceedings of the IEEE, vol. 108, no. 8, pp. 1338–1352, 2019.

M. Martonosi and M. Roetteler, “Next steps in quantum computing: Computer science’s role,” arXiv preprint arXiv:1903.10541, 2019.

Rayhan and S. Rayhan, “Quantum computing and ai: A quantum leap in intelligence,” 2023.

S. K. Singh, A. E. Azzaoui, M. M. Salim, and J. H. Park, “Quantum communication technology for future ict-review,” Journal of Information Processing Systems, vol. 16, no. 6, pp. 1459–1478, 2020.




How to Cite

Jabin Geevarghese George. (2024). Quantum Computing Improves Efficiency and Productivity in Financial Institutions. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3037 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5959



Research Article