This research aims into the transformative potential of quantum computing in the financial sector, underlining its ability to revolutionize risk management, portfolio optimization, and encryption methods. The integration of quantum technology with classical systems is emphasized as a symbiotic relationship that enhances efficiency and precision in financial tasks. A systematic literature review methodology is employed to investigate the development of the financial sector with quantum computing, A total of 186 studies were identified and 13 are selected and included in the review. These 13 journals were selected based on their relevance to the research topic, the quality and impact factor of the journal, the reputation of the authors who publish in the journal, and the rigor of the peer review process. The result indicates the integration of quantum technology in finance holds the potential to revolutionize trading optimization, risk management, and compliance processes, positioning early adopters at the forefront of innovation. The study advocates for sustained efforts in quantum machine learning and reinforcement learning applications to bridge the gap between theoretical potential and practical realization in the financial domain.
This research aims into the transformative potential of quantum computing in the financial sector, underlining its ability to revolutionize risk management, portfolio optimization, and encryption methods. The integration of quantum technology with classical systems is emphasized as a symbiotic relationship that enhances efficiency and precision in financial tasks. A systematic literature review methodology is employed to investigate the development of the financial sector with quantum computing, A total of 186 studies were identified and 13 are selected and included in the review. These 13 journals were selected based on their relevance to the research topic, the quality and impact factor of the journal, the reputation of the authors who publish in the journal, and the rigor of the peer review process. The result indicates the integration of quantum technology in finance holds the potential to revolutionize trading optimization, risk management, and compliance processes, positioning early adopters at the forefront of innovation. The study advocates for sustained efforts in quantum machine learning and reinforcement learning applications to bridge the gap between theoretical potential and practical realization in the financial domain.
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 technology is evolving rapidly with a potential to influence financial services. While quantum computing offers strong prospects for improving predictive analytics, risk management, and portfolio optimisation, its integration into financial systems remains constrained by algorithmic redesign requirements and technological limitations. This study reviews and synthesises research at the intersection of quantum computing and finance to map prevailing trends, thematic concentrations, and future inquiry paths. A systematic search of Web of Science and Scopus (2000–2023) identified 75 relevant studies, which were analysed using VOSViewer and Biblioshiny. The analysis shows that scholarly interest in quantum applications to finance accelerates only after 2020, indicating a temporal lag relative to the broader quantum computing literature. Four thematic clusters emerge: quantum cryptography and security, financial data processing, quantum algorithms for finance, and quantum machine learning. These areas reflect differing levels of maturity but reveal a shared emphasis on high-complexity modelling, enhanced data processing, and secure transaction frameworks. The review underscores the need for deeper algorithmic refinement, robust validation of financial use cases, and interdisciplinary collaboration among physicists, computer scientists, and finance researchers. Advancing these fronts will be essential for translating quantum potential into practical, scalable financial innovations.
Abstract The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies. These advances span fundamental theoretical inquiries into quantum information and the exploration of diverse applications arising from this evolving quantum computing paradigm. The scope of the related research is notably diverse. This paper consolidates and presents quantum computing research related to the financial sector. The finance applications considered in this study include portfolio optimization, fraud detection, and Monte Carlo methods for derivative pricing and risk calculation. In addition, we provide a comprehensive analysis of quantum computing’s applications and effects on blockchain technologies, particularly in relation to cryptocurrencies, which are central to financial technology research. As discussed in this study, quantum computing applications in finance are based on fundamental quantum physics principles and key quantum algorithms. This review aims to bridge the research gap between quantum computing and finance. We adopt a two-fold methodology, involving an analysis of quantum algorithms, followed by a discussion of their applications in specific financial contexts. Our study is based on an extensive review of online academic databases, search tools, online journal repositories, and whitepapers from 1952 to 2023, including CiteSeerX, DBLP, ResearchGate, Semantic Scholar, and scientific conference publications. We present state-of-the-art findings at the intersection of finance and quantum technology and highlight open research questions that will be valuable for industry practitioners and academicians as they shape future research agendas.
Human intellectual restlessness originates from the need for knowledge of the modern world. The financial world is struggling to prototype accurate and fast data at low risk. The quantum approach to finance can support this desire. The goal of this paper is to provide a comprehensive review of the literature on how quantum computing can be used in finance. This research aims to expose an architecture of the state of the art in quantum finance. In terms of methodology, the PSALSAR framework was used to conduct this systematic literature review. The selection procedure followed the PRISMA guidelines and was applied in two databases (Web of Science and Scopus) without time limit. In total, 94 out of 1646 articles were included for data extraction and assessment of content evaluation covering the period 2001-2023. The current review of quantum finance literature is structured around the following themes: journals, research methods, tested data series, research topics in quantum finance, and future research directions. Within the financial sector, quantum computing is used in three main areas: simulation, optimization, and machine learning. These areas are supported by algorithms that have been created in recent years. Finally, we propose to highlight the benefits and the applications of quantum finance and to stimulate the interest in the future prospects of the debates.
QC is revolutionizing finance this outlines its capability in tackling complicate computations in risk management, portfolio allocation and cryptography. The paper aims to discuss and review the advances in applications of QC in finance focusing on issues and prospects on its developments. They are quantum algorithms for financial modeling and optimization, quantum cryptography and computing, quantum supervised and unsupervised machine learning. Further, this work explores the challenges owing to restricted hardware capabilities, the problem with the algorithms used, and restraining factors in the industry. In this paper, they discussed the road map of integration of QC into the financial sector and analyzed the generation of theoretical knowledge and practical application of the same in preparation of introducing a new continent of financial innovation.
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.