Abstract Quantum computing allows a significant speed-up over traditional CPU- and GPU-based algorithms when applied to particular mathematical challenges such as optimisation and simulation. Despite promising advances and extensive research in hard- and software developments, currently available quantum systems are still largely limited in their capability. In line with this, practical applications in quantitative finance are still in their infancy. This paper analyses requirements and concrete approaches for the application to risk management in a financial institution. On the examples of Value-at-Risk for market risk and Potential Future Exposure for counterparty credit risk, the main contribution lies in going beyond textbook illustrations and instead exploring must-have model features and their quantum implementations. While conceptual solutions and small-scale circuits are feasible at this stage, the leap needed for real-life applications is still significant. In order to build a usable risk measurement system, the hardware capacity—measured in number of qubits—would need to increase by several magnitudes from their current value of about $$10^2$$ 10 2 . Quantum noise poses an additional challenge, and research into its control and mitigation would need to advance in order to render risk measurement applications deployable in practice. Overall, given the maturity of established classical simulation-based approaches that allow risk computations in reasonable time and with sufficient accuracy, the business case for a move to quantum solutions is not very strong at this point.
Abstract Quantum computing allows a significant speed-up over traditional CPU- and GPU-based algorithms when applied to particular mathematical challenges such as optimisation and simulation. Despite promising advances and extensive research in hard- and software developments, currently available quantum systems are still largely limited in their capability. In line with this, practical applications in quantitative finance are still in their infancy. This paper analyses requirements and concrete approaches for the application to risk management in a financial institution. On the examples of Value-at-Risk for market risk and Potential Future Exposure for counterparty credit risk, the main contribution lies in going beyond textbook illustrations and instead exploring must-have model features and their quantum implementations. While conceptual solutions and small-scale circuits are feasible at this stage, the leap needed for real-life applications is still significant. In order to build a usable risk measurement system, the hardware capacity—measured in number of qubits—would need to increase by several magnitudes from their current value of about 102 10 2 . Quantum noise poses an additional challenge, and research into its control and mitigation would need to advance in order to render risk measurement applications deployable in practice. Overall, given the maturity of established classical simulation-based approaches that allow risk computations in reasonable time and with sufficient accuracy, the business case for a move to quantum solutions is not very strong at this point.
The rapid advancement of quantum computing is poised to revolutionize industries, particularly in areas requiring complex computational power. This paper explores the potential impact of quantum computing on financial risk management from a business perspective. Traditional risk management models struggle to cope with the increasing complexity of global financial systems, making quantum computing a promising solution. By leveraging quantum algorithms, financial institutions can optimize risk assessment, enhance portfolio management, and significantly improve predictive models. This paper examines key algorithms and their applications in finance, discusses the challenges and opportunities in adopting quantum solutions, and presents case studies of early adopters. The findings suggest that quantum computing can offer a competitive advantage to businesses by providing superior risk analysis capabilities. However, limitations in technology, infrastructure, and expertise remain significant hurdles. This study contributes to the growing discourse on quantum computing by providing actionable insights for businesses looking to integrate quantum solutions into their risk management strategies.
Quantum computing is emerging as a transformative technology in financial portfolio optimization, offering the potential to process complex calculations at unprecedented speeds. This study explores the impact of quantum computing on financial decision-making through a case-based qualitative approach, analyzing real-world applications in leading financial institutions such as JPMorgan Chase, Goldman Sachs, and BBVA. The research highlights how quantum algorithms, including Quantum Approximate Optimization Algorithm (QAOA), Quantum Monte Carlo (QMC), and quantum annealing, are being utilized for risk management, asset pricing, and credit risk assessment. Findings indicate that while quantum computing enhances computational efficiency, scenario analysis, and optimization models, its widespread adoption is hindered by hardware limitations, high error rates, and regulatory uncertainties. The study emphasizes the growing relevance of hybrid quantum-classical models as a practical solution for near-term applications. Future advancements in quantum hardware, algorithm development, and financial cryptography are expected to drive broader adoption in financial markets. This research contributes to the ongoing discourse on quantum finance by providing empirical insights into the challenges and opportunities associated with quantum computing in portfolio optimization. The study concludes that while quantum computing is not yet fully commercialized, its long-term implications for financial risk assessment, high-frequency trading, and decision-making are profound.
Quantum computing is emerging as a transformative technology in financial portfolio optimization, offering the potential to process complex calculations at unprecedented speeds. This study explores the impact of quantum computing on financial decision-making through a case-based qualitative approach, analyzing real-world applications in leading financial institutions such as JPMorgan Chase, Goldman Sachs, and BBVA. The research highlights how quantum algorithms, including Quantum Approximate Optimization Algorithm (QAOA), Quantum Monte Carlo (QMC), and quantum annealing, are being utilized for risk management, asset pricing, and credit risk assessment. Findings indicate that while quantum computing enhances computational efficiency, scenario analysis, and optimization models, its widespread adoption is hindered by hardware limitations, high error rates, and regulatory uncertainties. The study emphasizes the growing relevance of hybrid quantum-classical models as a practical solution for near-term applications. Future advancements in quantum hardware, algorithm development, and financial cryptography are expected to drive broader adoption in financial markets. This research contributes to the ongoing discourse on quantum finance by providing empirical insights into the challenges and opportunities associated with quantum computing in portfolio optimization. The study concludes that while quantum computing is not yet fully commercialized, its long-term implications for financial risk assessment, high-frequency trading, and decision-making are profound.
Quantum computing is revolutionizing computational methods in finance by enhancing efficiency and accuracy in financial modeling and risk management. This review explores the impact of quantum computing in finance, focusing on derivative pricing, risk management, and portfolio optimization. It analyzes the benefits, limitations, and future implications of integrating quantum technologies with classical financial systems. A comprehensive review of quantum Monte Carlo methods, quantum algorithms, and their financial applications was conducted. Theoretical foundations, including Chebyshev’s inequality and Grover’s search method, were evaluated for their role in enhancing sampling efficiency and predictive accuracy. Empirical studies and use cases demonstrate the practical implications of quantum computing in pricing financial derivatives and managing risk through Value at Risk and Conditional Value at Risk assessments. The critical findings reveal that Quantum Monte Carlo algorithms provide substantial efficiency gains, reducing sample size requirements by up to fourfold compared to classical methods. Techniques employed in prior studies highlight the effectiveness of Quantum Amplitude Estimation for derivative pricing and risk analysis. However, challenges persist in scalability, data quality, integration with classical systems, and compliance with regulatory standards. This research contributes to the current body of knowledge by providing a comprehensive analysis of quantum algorithms’ practical applications in derivative pricing, risk management, and portfolio optimization, demonstrating the efficiency gains and limitations of quantum approaches. Future research should explore hybrid quantum–classical frameworks, quantum applications in blockchain, and enhanced quantum cryptography to ensure secure and efficient financial transactions.
This review paper examines the potential implications of quantum computing for financial risk management. It explores the fundamental principles of quantum computing, including qubits, superposition, and entanglement. It discusses its advantages over classical computing for risk assessment and mitigation. The paper outlines traditional approaches to financial risk management. It explores how quantum algorithms, such as quantum Monte Carlo methods and quantum annealing, can enhance these strategies. Challenges and barriers to adopting quantum computing in the financial industry are identified, along with future research directions. Ultimately, the paper highlights the transformative potential of quantum computing for improving risk management in today's complex financial markets. Keywords: Quantum Computing, Financial Risk Management, Qubits, Quantum Algorithms, Monte Carlo Simulations, Portfolio Optimisation.
This research aims at determining the use of quantum computing on financial structures, mainly pertaining to portfolio, derivatives, Monte Carlo assessments, and risk analyses. The problems appear within the context of the continually development of the financial industry where the usage of the classic methods when solving the problems associated with large data set and the application of complex models can be slow. There are two types of quantum computations: the first is a classical computation that uses an approach very close to a Turing machine; the second is quantum parallelism that enables a system to compute an unlimited number of possibilities simultaneously and will not suffer from these limitations. The study points out that there are several application areas where quantum algorithms provide solutions better than classical ones, for instance, in cutting computational time for portfolio, and providing better solution in derivative pricing. Applying quantum algorithms, financial systems can provide increased precision and liabilities management, as well as improved decision making. The paper also discusses future applications of quantum computing in fraud and security detection, which would both significantly benefit from quantum methods. A still experimental field, the optimisation study does show that Quantum could bring significant changes in the techniques used by the financial industry to address its most critical issues