Quantum-computers can assist to resolve and answer particular problems and issues which are not solvable by binary computers or classical hardware. Considering the prompt response offered by quantum-computers hence has the possibility to assist any sector, market or division which utilizes estimations, such as financial markets. Initially, the quantum-computer applications for financial markets included, machine-learning, optimization and simulation issues which have been discussed by most of the studies. Therefore, in this study we aim to analyze the use-cases like risk management, portfolio management and pricing of derivatives to provide insight about the usefulness of quantum-computers in financial markets. This indicates that financial markets can benefit from quantum-computers in the upcoming years.
Quantum-computers can assist to resolve and answer particular problems and issues which are not solvable by binary computers or classical hardware. Considering the prompt response offered by quantum-computers hence has the possibility to assist any sector, market or division which utilizes estimations, such as financial markets. Initially, the quantum-computer applications for financial markets included, machine-learning, optimization and simulation issues which have been discussed by most of the studies. Therefore, in this study we aim to analyze the use-cases like risk management, portfolio management and pricing of derivatives to provide insight about the usefulness of quantum-computers in financial markets. This indicates that financial markets can benefit from quantum-computers in the upcoming years.
Quantum computers can solve specific problems that are not feasible on "classical" hardware. Harvesting the speed-up provided by quantum computers therefore has the potential to change any industry which uses computation, including finance. First quantum applications for the financial industry involving optimization, simulation, and machine learning problems have already been proposed and applied to use cases such as portfolio management, risk management, and pricing derivatives. This survey reviews platforms, algorithms, methodologies, and use cases of quantum computing for various applications in finance in a structured way. It is aimed at people working in the financial industry and serves to gain an overview of the current development and capabilities and understand the potential of quantum computing in the financial industry.
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
Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning, describing how these solutions, adapted to work on a quantum computer, can potentially help to solve financial problems, such as derivative pricing, risk modeling, portfolio optimization, natural language processing, and fraud detection, more efficiently and accurately. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.
In recent years, advancements in classical computing have grown rapidly; however, quantum computing is expected to surpass traditional computational limits and revolutionize multiple industries. Among these, the financial sector is anticipated to be one of the earliest beneficiaries, particularly for medium‑ and long‑term analytical tasks. This study investigates the role of quantum computation in financial modeling and portfolio optimization, emphasizing the value of classical convex optimization algorithms integrated with emerging quantum methods. Key areas in which quantum computing demonstrates potential include machine learning–based optimization, stochastic analysis, fraud detection, natural language processing, portfolio construction, risk estimation, and derivative valuation. Moreover, the practicality of deploying such algorithms on near‑term quantum hardware is explored, along with the suitability of various architectures for diverse financial use cases.
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.
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.