Quantum Computing and Financial Risk Analytics: Exploring New Approaches to Complex Economic Modelling

Enrique Martín López

Quantum Computing and Financial Risk Analytics: Exploring New Approaches to Complex Economic Modelling

Author(s)	Prof. Enrique Martín López
Country	United States
Abstract	Financial risk analytics relies increasingly on computationally intensive models that must process large numbers of correlated variables, uncertain market conditions, nonlinear financial instruments, and rapidly changing economic scenarios. Classical techniques such as Monte Carlo simulation, stochastic optimization, stress testing, and scenario analysis remain fundamental to financial risk management, but their computational requirements can become substantial when portfolios contain complex derivatives, multiple risk factors, or strict accuracy requirements. Quantum computing offers a different computational framework based on superposition, interference, entanglement, and probabilistic measurement. These properties have encouraged research into quantum algorithms for portfolio optimization, derivative valuation, probability estimation, credit-risk analysis, and systemic-risk modelling. This paper investigates the potential role of quantum computing in financial risk analytics through a conceptual review and a transparent theoretical simulation. The study compares the convergence behavior of classical Monte Carlo estimation, represented by O(M−1/2), with ideal quantum amplitude estimation, represented by O(M−1). The theoretical comparison indicates that quantum amplitude estimation can reduce estimation error more rapidly as the number of algorithmic queries increases. This mathematical advantage does not, however, establish immediate practical superiority because state preparation, circuit depth, hardware noise, model validation, data loading, and fault-tolerance requirements can substantially affect real performance. The paper identifies portfolio optimization, derivative pricing, Value at Risk estimation, Conditional Value at Risk analysis, credit-risk simulation, and interconnected-market modelling as promising application domains. It concludes that near-term progress is most likely to emerge from hybrid quantum–classical architectures that use quantum routines for carefully selected computational bottlenecks while retaining classical systems for data preparation, governance, validation, reporting, and regulatory control. Responsible implementation requires transparent benchmarking, auditability, cybersecurity, model-risk management, and cautious communication of quantum performance claims.
Keywords	quantum computing; financial risk analytics; quantum amplitude estimation; Monte Carlo simulation; portfolio optimization; Value at Risk; economic modelling; hybrid quantum–classical systems.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-23

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Quantum Computing and Financial Risk Analytics: Exploring New Approaches to Complex Economic Modelling

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