In terms of computing capacity, quantum computers are expected to surpass conventional computers throughout this decade and have a seismic impact on a number of commercial sectors, particularly finance. In actuality, it is anticipated that the financial industry would reap both immediate and long-term benefits from quantum computing. The current state of quantum computing for financial applications is thoroughly evaluated in this survey research, with a focus on stochastic modelling, optimisation, and machine learning. It demonstrates how these methods could aid in more rapidly and efficiently resolving financial issues including derivative pricing, risk modelling and fraud detection if they were updated to operate on a quantum computer.The possibility of quantum computing as a ground-breaking tool for streamlining financial engineering procedures is examined in this research. Growing interest in using quantum computing to solve these problems is being driven by the complexity of financial systems and the computational rigor of financial engineering problems. The effectiveness of quantum computing in advancing optimization techniques in the field of financial engineering is assessed in this paper.
In terms of computing capacity, quantum computers are expected to surpass conventional computers throughout this decade and have a seismic impact on a number of commercial sectors, particularly finance. In actuality, it is anticipated that the financial industry would reap both immediate and long-term benefits from quantum computing. The current state of quantum computing for financial applications is thoroughly evaluated in this survey research, with a focus on stochastic modelling, optimisation, and machine learning. It demonstrates how these methods could aid in more rapidly and efficiently resolving financial issues including derivative pricing, risk modelling and fraud detection if they were updated to operate on a quantum computer.The possibility of quantum computing as a ground-breaking tool for streamlining financial engineering procedures is examined in this research. Growing interest in using quantum computing to solve these problems is being driven by the complexity of financial systems and the computational rigor of financial engineering problems. The effectiveness of quantum computing in advancing optimization techniques in the field of financial engineering is assessed in this paper.
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.
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 computing has emerged as a revolutionary computational paradigm capable of solving complex optimization and probabilistic problems that are computationally expensive for classical computers. The financial industry, characterized by large-scale datasets, uncertainty, stochastic behavior, and multidimensional optimization challenges, is increasingly exploring quantum computing to enhance decision-making processes. This paper presents a comprehensive investigation into the applications of quantum computing in financial modeling, emphasizing portfolio optimization, derivative pricing, risk assessment, fraud detection, and algorithmic trading. Unlike conventional computational techniques that suffer from exponential complexity when processing high-dimensional financial data, quantum algorithms leverage superposition, entanglement, and quantum parallelism to improve computational efficiency and solution quality. The study discusses fundamental quantum computing concepts, analyzes existing financial modeling frameworks, and examines how quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Monte Carlo (QMC), Grover’s Search Algorithm, and Quantum Machine Learning (QML) contribute to solving real-world financial problems. Furthermore, the paper reviews recent research developments, identifies implementation challenges associated with noisy intermediate-scale quantum (NISQ) devices, and highlights future opportunities for hybrid quantum-classical financial systems. The findings demonstrate that quantum computing has significant potential to transform computational finance by improving prediction accuracy, accelerating optimization tasks, and enabling scalable financial analytics for next-generation intelligent financial ecosystems.
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.
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.