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.
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.
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.
This paper provides an in-depth review of the evolving role of quantum computing in the financial sector, emphasizing both its computational potential and cybersecurity implications. Distinguishing itself from existing surveys, this work integrates classical quantum computing applications - such as portfolio optimization, risk analysis, derivative pricing, and Monte Carlo simulations with a thorough examination of blockchain technologies and post-quantum cryptography (PQC), which are crucial for maintaining secure financial operations in the emerging quantum era. We propose a structured four-step framework to assess the feasibility and expected benefits of implementing quantum solutions in finance, considering factors such as computational scalability, error tolerance, data complexity, and practical implementability. This framework is applied to a series of representative financial scenarios to identify domains where quantum approaches can surpass classical techniques. Furthermore, the paper explores the vulnerabilities quantum computing introduces to digital finance-related applications and blockchain security, including risks to digital signatures, hash functions, and randomness generation, and discusses mitigation strategies through PQC and quantum-resilient alternatives of classical digital finance tools and blockchain architectures. By addressing both quantum blockchain, quantum key distribution (QKD) as well as quantum communication networks, his review presents a more holistic perspective than prior studies, offering actionable insights for researchers, financial practitioners, and policymakers navigating the intersection of quantum computing, blockchain, and secure financial systems.
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 computers pose a significant threat to blockchain technology's security, which heavily relies on public-key cryptography and hash functions. The cryptographic algorithms used in blockchains, based on large odd prime numbers and discrete logarithms, can be easily compromised by quantum computing algorithms like Shor's algorithm and its future qubit variations. This survey paper comprehensively examines the impact of quantum computers on blockchain security and explores potential mitigation strategies. We begin by surveying the existing literature on blockchains and quantum computing, providing insights into the current state of research. We then present an overview of blockchain, highlighting its key components and functionalities. We delve into the preliminaries and key definitions of quantum computing, establishing a foundation for understanding the implications on blockchain security. The application of blockchains in cybersecurity is explored, considering their strengths and vulnerabilities in light of evolving quantum computing capabilities. The survey focuses on the quantum security of blockchain's fundamental building blocks, including digital signatures, hash functions, consensus algorithms, and smart contracts. We analyze the vulnerabilities introduced by quantum computers and discuss potential countermeasures and enhancements to ensure the integrity and confidentiality of blockchain systems. Furthermore, we investigate the quantum attack surface of blockchains, identifying potential avenues for exploiting quantum computing to strengthen existing attacks. We emphasize the need for developing quantum-resistant defenses and explore solutions for mitigating the threat of quantum computers to blockchains, including the adoption of quantum and post-quantum blockchain architectures. By examining vulnerabilities and discussing mitigation strategies, we aim to guide researchers, practitioners, and policymakers in developing robust and secure blockchain systems capable of withstanding advancements in quantum computing technology.
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 pose a significant threat to blockchain technology’s security, which heavily relies on public-key cryptography and hash functions. The cryptographic algorithms used in blockchains, based on large odd prime numbers and discrete logarithms, can be easily compromised by quantum computing algorithms like Shor’s algorithm and its future qubit variations. This survey paper comprehensively examines the impact of quantum computers on blockchain security and explores potential mitigation strategies. We begin by surveying the existing literature on blockchains and quantum computing, providing insights into the current state of research. We then present an overview of blockchain, highlighting its key components and functionalities. We delve into the preliminaries and key definitions of quantum computing, establishing a foundation for understanding the implications on blockchain security. The application of blockchains in cybersecurity is explored, considering their strengths and vulnerabilities in light of evolving quantum computing capabilities. The survey focuses on the quantum security of blockchain’s fundamental building blocks, including digital signatures, hash functions, consensus algorithms, and smart contracts. We analyze the vulnerabilities introduced by quantum computers and discuss potential countermeasures and enhancements to ensure the integrity and confidentiality of blockchain systems. Furthermore, we investigate the quantum attack surface of blockchains, identifying potential avenues for exploiting quantum computing to strengthen existing attacks. We emphasize the need for developing quantum-resistant defenses and explore solutions for mitigating the threat of quantum computers to blockchains, including the adoption of quantum and post-quantum blockchain architectures. By examining vulnerabilities and discussing mitigation strategies, we aim to guide researchers, practitioners, and policymakers in developing robust and secure blockchain systems capable of withstanding advancements in quantum computing technology.