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.
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.
In this paper, we provide an overview of the recent work in the quantum finance realm from various perspectives. The applications in consideration are Portfolio Optimization, Fraud Detection, and Monte Carlo methods for derivative pricing and risk calculation. Furthermore, we give a comprehensive overview of the applications of quantum computing in the field of blockchain technology which is a main concept in fintech. In that sense, we first introduce the general overview of blockchain with its main cryptographic primitives such as digital signature algorithms, hash functions, and random number generators as well as the security vulnerabilities of blockchain technologies after the merge of quantum computers considering Shor's quantum factoring and Grover's quantum search algorithms. We then discuss the privacy preserving quantum-resistant blockchain systems via threshold signatures, ring signatures, and zero-knowledge proof systems i.e. ZK-SNARKs in quantum resistant blockchains. After emphasizing the difference between the quantum-resistant blockchain and quantum-safe blockchain we mention the security countermeasures to take against the possible quantumized attacks aiming these systems. We finalize our discussion with quantum blockchain, efficient quantum mining and necessary infrastructures for constructing such systems based on quantum computing. This review has the intention to be a bridge to fill the gap between quantum computing and one of its most prominent application realms: Finance. We provide the state-of-the-art results in the intersection of finance and quantum technology for both industrial practitioners and academicians.
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 has emerged as a transformative computational paradigm, offering novel capabilities to tackle complex problems in finance—most notably in derivative pricing, risk management, and portfolio optimization. This review synthesizes recent advances in quantum algorithms and hybrid quantum–classical frameworks as applied to financial services, examining how technologies such as quantum-annealing, quantum amplitude estimation, and variational quantum circuits can improve efficiency, accuracy, and scalability over classical methods. The study provides a systematic assessment of key use-cases: (1) derivative pricing and greeks computation via quantum Monte Carlo and amplitude estimation; (2) risk management enhancements, including Value-at-Risk (VaR) and conditional VaR through quantum gradient estimation; and (3) portfolio optimization via QUBO (quadratic unconstrained binary optimization) mapping, the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing approaches. In addition to reviewing technological potential, the paper addresses critical barriers—hardware noise, coherence limitations, data integration, regulatory considerations—and outlines future research directions, including quantum-resilient cryptography in finance and industry coupling. By bringing together algorithmic, financial and regulatory perspectives, this review seeks to guide academics and practitioners toward the next frontier in financial computing.
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.
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.
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.