Open Access

ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING

https://doi.org/10.55640/ijidml-v02i02-01

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Ananya Patel (Ph.D. Candidate) ¹ ,

⁴ Department of Computer Science and Engineering, Indian Institute of Technology Bombay, India

Abstract

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.

Keywords

Quantum machine learning, financial prediction, quantum computing, time-series forecasting

References

Herman D, Googin C, Liu X, Galda A, Safro I, Sun Y, Pistoia M, Alexeev Y (2022) A survey of quantum computing for finance. Papers 2201.02773, arXiv.org. https://ideas.repec.org/p/arx/papers/2201.02773.html

Egger DJ, Gambella C, Marecek J, McFaddin S, Mevissen M, Raymond R, Simonetto A, Woerner S, Yndurain E (2020) Quantum computing for finance: State-of-the-art and future prospects.37 IEEE Transactions on Quantum Engineering 1:1–24. https://doi.org/10.1109/TQE.2020.3030314

McKinsey & Company (2021) Quantum computing: an emerging ecosystem and industry use cases.38 Accessed 16 Feb 2023

Bouland A, Dam W, Joorati H, Kerenidis I, Prakash A (2020) Prospects and challenges of quantum finance. arXiv. https://doi.org/10.48550/ARXIV.2011.06492

Leclerc L, Ortiz-Guitierrez L, Grijalva S, Albrecht B, Cline JRK, Elfving VE, Signoles A, Henriet L, Del Bimbo G, Sheikh UA, Shah M, Andrea L, Ishtiaq F, Duarte A, Mugel S, Caceres I, Kurek M, Orus R, Seddik A, Hammammi O, Isselnane H, M’tamon D (2022) Financial Risk Management on a Neutral Atom Quantum Processor. arXiv. https://doi.org/10.48550/ARXIV.2212.03223

Rebentrost P, Lloyd S (2018) Quantum computational finance: quantum algorithm for portfolio optimization.39 arXiv:1811.03975

Kerenidis I, Prakash A, Szilágyi D (2019) Quantum algorithms for portfolio optimization. In: Proceedings of the 1st ACM conference on advances in financial technologies. AFT ’19, pp. 147–155. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3318041.3355465

Rebentrost P, Luongo A, Bosch S, Lloyd S (2022) Quantum computational finance: martingale asset pricing for incomplete markets.40 arXiv. https://doi.org/10.48550/ARXIV.2209.08867

Doriguello JaF, Luongo A, Bao J, Rebentrost P, Santha M (2022) Quantum algorithm for stochastic optimal stopping problems with applications in Finance.41 In: Le Gall F, Morimae T (eds) 17th conference on the theory of quantum computation, Communication and Cryptography (TQC 2022). Leibniz International Proceedings in Informatics (LIPIcs), vol 232, pp 2–1224. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany. https://doi.org/10.4230/LIPIcs.TQC.2022.2. https://drops.dagstuhl.de/opus/volltexte/2022/16509

Suzuki Y, Uno S, Raymond R, Tanaka T, Onodera T, Yamamoto N (2020) Amplitude estimation without phase estimation. Quantum Inf Process 19(2):75. https://doi.org/10.1007/s11128-019-2565-2

Giurgica-Tiron T, Kerenidis I, Labib F, Prakash A, Zeng W (2022) Low depth algorithms for quantum amplitude estimation.42 Quantum 6:745. https://doi.org/10.22331/q-2022-06-27-745

Pistoia M, Ahmad SF, Ajagekar A, Buts A, Chakrabarti S, Herman D, Hu S, Jena A, Minssen P, Niroula P, Rattew A, Sun Y, Yalovetzky R (2021) Quantum Machine Learning for Finance. arXiv. https://doi.org/10.48550/ARXIV.2109.04298

Emmanoulopoulos D, Dimoska S (2022) Quantum machine learning in finance: time series forecasting.43 arXiv e-prints, 2202

Alcazar J, Leyton-Ortega V, Perdomo-Ortiz A (2020) Classical versus quantum models in machine learning: insights from a finance application.44 Mach Learn Sci Technol 1(3):035003. https://doi.org/10.1088/2632-2153/ab9009

Nguyen N, Chen K-C (2022) Bayesian quantum neural networks. IEEE. Access 10:54110–54122. https://doi.org/10.1109/ACCESS.2022.3168675

Kerenidis I, Prakash A (2022) Quantum machine learning with subspace states.45 arXiv:2202.00054

Landman J, Mathur N, Li YY, Strahm M, Kazdaghli S, Prakash A, Kerenidis I (2022) Quantum methods for neural networks and application to medical image classification.46 Quantum 6:881. https://doi.org/10.22331/q-2022-12-22-881

Arjovsky M, Shah A, Bengio Y (2016) Unitary evolution recurrent neural networks.47 In: International conference on machine learning, PMLR, pp 1120–1128

Breiman L (2001) Random forests.48 Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

Kulesza A, Taskar B (2012) Determinantal point processes for machine learning.49 Found Trends® Mach Learn 5(2–3):123–286. https://doi.org/10.1561/2200000044

Macchi O (1975) The coincidence approach to stochastic point processes.50 Adv Appl Probab 7(1):83–122. https://doi.org/10.2307/1425855

Derezinski M, Mahoney MW (2021) Determinantal point processes in randomized numerical linear algebra.51 Not Am Math Soc 68(1):34–45

Bardenet R, Hardy A (2020) Monte carlo with determinantal point processes. Ann Appl Probab 30(1):368–417

Elfeki M, Couprie C, Riviere M, Elhoseiny M (2019) Gdpp: learning diverse generations using determinantal point processes.52 In: International conference on machine learning, PMLR, pp 1774–1783

Derezinski M (2018) Volume sampling for linear regression.53 UC Santa Cruz Electronic Theses and Dissertations 36

Dereziński M, Warmuth MK, Hsu D (2018) Leveraged volume sampling for linear regression.54 In: Proceedings of the 32nd international conference on neural information processing systems. NIPS’18, pp 2510–2519. Curran Associates Inc., Red Hook, NY, USA

Kulesza A, Taskar B (2011) K-dpps: fixed-size determinantal point processes.55 In: Proceedings of the 28th international conference on international conference on machine learning. ICML’11, pp 1193–1200. Omnipress, Madison, WI, USA

Hough JB, Krishnapur M, Peres Y, Virág B (2006) Determinantal Processes and Independence.56 Probab Surv 3(none), 206–229 https://doi.org/10.1214/154957806000000078

Anari N, Oveis Gharan S, Rezaei A (2016) Monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes.57 In: Feldman V, Rakhlin A, Shamir O (eds) 29th Annual conference on learning theory. proceedings of machine learning research, vol 49, pp 103–115. PMLR, Columbia University, New York, New York, USA. https://proceedings.mlr.press/v49/anari16.html

Li C, Sra S, Jegelka S (2016) Fast mixing markov chains for strongly rayleigh measures, dpps, and constrained sampling.58 In: Lee DD, Sugiyama M, Luxburg U, Guyon I, Garnett R (eds) Advances in neural information processing systems 29: annual conference on neural information processing systems 2016, December 5-10, 2016, Barcelona, Spain, pp 4188–4196. https://proceedings.neurips.cc/paper/2016/hash/850af92f8d9903e7a4e0559a98ecc857-Abstract.html

Derezinski M, Calandriello D, Valko M (2019) Exact sampling of determinantal point processes with sublinear time preprocessing.59 In: Wallach H, Larochelle H, Beygelzimer A, Alché-Buc F, Fox E, Garnett R (eds) Advances in neural information processing systems, vol 32. Curran Associates, Inc., Vancouver, Canada. https://proceedings.neurips.cc/paper/2019/file/fa3060edb66e6ff4507886f9912e1ab9-Paper.pdf

Calandriello D, Derezinski M, Valko M (2020) Sampling from a k-dpp without looking at all items.60 In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds) Advances in Neural Information Processing Systems, vo 33, pp 6889–6899. Curran Associates, Inc., Online. https://proceedings.neurips.cc/paper/2020/file/4d410063822cd9be28f86701c0bc3a31-Paper.pdf

Gautier G, Polito G, Bardenet R, Valko M (2019) Dppy: Dpp sampling with python.61 J Mach Learn Res 20(180):1–7

Kazdaghli S, Kerenidis I, Kieckbusch J, Teare P (2023) Improved clinical data imputation via classical and quantum determinantal point processes.62 arXiv:2303.17893

Grinsztajn L, Oyallon E, Varoquaux G (2022) Why do tree-based models still outperform deep learning on typical tabular data? In: Thirty-sixth conference on neural information processing systems datasets and benchmarks track. https://openreview.net/forum?id=Fp7__phQszn

Benedetti M, Lloyd E, Sack S, Fiorentini M (2019) Parameterized quantum circuits as machine learning models. Quantum Science and Technology 4(4):043001. https://doi.org/10.1088/2058-9565/ab4eb5, arXiv:1906.07682 [quant-ph]

Havlíček V, Córcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM (2018) Supervised learning with quantum-enhanced feature spaces. Nature 567:209–212

Liu Y, Arunachalam S, Temme K (2021) A rigorous and robust quantum speed-up in supervised machine learning.63 Nat Phys 17(9):1013–1017. https://doi.org/10.1038/s41567-021-01287-z, arXiv:2010.02174 [quant-ph]

Jia K, Li S, Wen Y, Liu T, Tao D (2019) Orthogonal deep neural networks. IEEE Trans Pattern Anal Mach Intell

Johri S, Debnath S, Mocherla A, Singh A, Prakash A, Kim J, Kerenidis I (2021) Nearest centroid classification on a trapped ion quantum computer.64 npj Quantum Information (to appear), arXiv:2012.04145

Jozsa R, Miyake A (2008) Matchgates and classical simulation of quantum circuits.65 Proc Royal Soc A: Math Phys Eng Sci 464(2100):3089–3106. https://doi.org/10.1098/rspa.2008.0189

Cherrat EA, Kerenidis I, Mathur N, Landman J, Strahm M, Li YY (2022) Quantum vision transformers.66 arXiv:2209.08167

Buuren S, Groothuis-Oudshoorn K (2011) mice: multivariate imputation by chained equations in r. J Stat Softw 45(3):1–67. https://doi.org/10.18637/jss.v045.i03

contributors Q (2021) Qiskit: an open-source framework for quantum computing. https://doi.org/10.5281/zenodo.2573505

Li G, Ding Y, Xie Y (2018) Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices.67 arXiv e-prints, 1809–02573. https://doi.org/10.48550/arXiv.1809.02573 [cs.ET]

Viola L, Lloyd S (1998) Dynamical suppression of decoherence in two state quantum systems.68 Phys Rev A 58, 2733. https://doi.org/10.1103/PhysRevA.58.2733, arXiv:quant-ph/9803057

Ezzell N, Pokharel B, Tewala L, Quiroz G, Lidar DA (2022) Dynamical decoupling for superconducting qubits: a performance survey.69 arXiv:2207.03670 [quant-ph]

Nation PD, Kang H, Sundaresan N, Gambetta JM (2021) Scalable mitigation of measurement errors on quantum computers.70 PRX Quantum 2, 040326 https://doi.org/10.1103/PRXQuantum.2.040326, arXiv:2108.12518 [quant-ph]

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ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING

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