This paper systematically reviews the historical development trajectory and current situation of quantum financial theory research through an in-depth review of relevant literature. This paper summarizes the history of the concept of quantum finance, elaborates on the development stages of quantum finance, and explains the contributions of representative work in different periods in various aspects, such as theoretical frameworks, mathematical models, and algorithmic tools, which is of great significance to the research path of quantum finance. In addition, the paper introduces in detail the research progress of domestic and foreign body problems, such as quantum algorithm optimization of investment portfolios, quantitative pricing of derivatives, quantum neural networks, etc., laying a good foundation for future in-depth study of this interdisciplinary subject. Overall, this paper systematically reviews the development history of quantum finance research, provides readers with a macro understanding framework by sorting out the progress in different research fields, and has significant reference value for interdisciplinary exchanges.
This paper systematically reviews the historical development trajectory and current situation of quantum financial theory research through an in-depth review of relevant literature. This paper summarizes the history of the concept of quantum finance, elaborates on the development stages of quantum finance, and explains the contributions of representative work in different periods in various aspects, such as theoretical frameworks, mathematical models, and algorithmic tools, which is of great significance to the research path of quantum finance. In addition, the paper introduces in detail the research progress of domestic and foreign body problems, such as quantum algorithm optimization of investment portfolios, quantitative pricing of derivatives, quantum neural networks, etc., laying a good foundation for future in-depth study of this interdisciplinary subject. Overall, this paper systematically reviews the development history of quantum finance research, provides readers with a macro understanding framework by sorting out the progress in different research fields, and has significant reference value for interdisciplinary exchanges.
The paper provides an integrated literature review of recent scientific publications on quantum computing in finance and identifies promising directions for future research on the subject. The review covers seven thematic areas: portfolio optimization, derivative pricing and stochastic volatility, quantum machine learning for fraud detection and credit risk, insurance and actuarial science, mixed-frequency econometrics, fuzzy-quantum approaches for financial explainability, and security of cryptocurrencies. The paper compiles the essential quantum computational methods proposed in the literature, outlines their economic significance and the existing constraints for empirical testing and implementation, and discusses cross-cutting issues of explainability, trustworthy AI, robustness, and governance that arise across these application domains. Drawing on this review, the paper identifies five macro-gaps in the existing literature and proposes seven concrete directions for future research, grounded in European financial data and currently available quantum computing infrastructure. A special focus throughout is the increasingly available quantum infrastructure in Europe and the regulatory emphasis on trustworthy artificial intelligence, both of which create timely opportunities for future applications in financial modelling, risk management, and explainable financial AI.
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.
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.
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.
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 transitioned from a largely theoretical construct into a rapidly evolving technological paradigm with significant implications for computational finance and financial data science. This article presents a comprehensive and integrative research synthesis examining the convergence of quantum algorithms, quantum machine learning, and probabilistic modeling techniques—particularly determinantal point processes—in the context of financial optimization, risk management, asset pricing, and data-driven decision-making. Drawing exclusively from a curated body of foundational and contemporary literature, the study explores how quantum computational advantages may address long-standing computational bottlenecks in finance, including high-dimensional optimization, stochastic simulation, portfolio construction, and time-series forecasting under uncertainty. The article begins by situating quantum finance within the broader evolution of financial computation, emphasizing the limitations of classical Monte Carlo methods, convex optimization frameworks, and deep learning architectures when confronted with combinatorial complexity and structural constraints inherent in modern financial systems. It then provides an extensive theoretical elaboration of quantum algorithms relevant to finance, including amplitude estimation, quantum portfolio optimization, stochastic optimal stopping, and martingale-based asset pricing in incomplete markets. Particular attention is devoted to algorithmic primitives such as low-depth amplitude estimation and near-term implementations on noisy intermediate-scale quantum hardware, highlighting their feasibility and constraints. A substantial portion of the article is dedicated to quantum machine learning models and their classical counterparts, examining parameterized quantum circuits, quantum-enhanced feature spaces, Bayesian quantum neural networks, and subspace-based learning. These models are critically compared with established classical approaches such as random forests, orthogonal neural networks, and tree-based ensembles, especially in the context of tabular financial data. The discussion is further enriched by an in-depth theoretical treatment of determinantal point processes as a unifying probabilistic framework for diversity-aware sampling, feature selection, and data imputation, including their classical and emerging quantum formulations. By synthesizing insights across quantum algorithms, machine learning theory, and financial engineering, this article articulates both the transformative potential and the unresolved challenges of quantum finance. It concludes with a forward-looking discussion on scalability, hardware constraints, hybrid quantum-classical workflows, and the epistemic implications of adopting quantum models in financial decision-making. The work aims to serve as a foundational reference for researchers and practitioners seeking a deep theoretical understanding of quantum computational finance as an emerging interdisciplinary field.