Quantum computation holds the promise of highly efficient algorithms, provides exponential speedups for specific technologically significant problems, plays the most promising role in quantum technological progress, and has transformative potential in numerous industry sectors. In quantum computing, finance is recognized as one of the most beneficial areas because of the potential for many financial cases that can be solved by quantum algorithms suitable for noisy-intermedia-scale quantum(NISQ) computers. The benefits of quantum computing are colossal time and computational memory reduction with which the computational tasks are performed, which leads to the accuracy of the computations. This paper presents a comprehensive summary of quantum computing for traditional and Blockchain financial applications.
Quantum computation holds the promise of highly efficient algorithms, provides exponential speedups for specific technologically significant problems, plays the most promising role in quantum technological progress, and has transformative potential in numerous industry sectors. In quantum computing, finance is recognized as one of the most beneficial areas because of the potential for many financial cases that can be solved by quantum algorithms suitable for noisy-intermedia-scale quantum(NISQ) computers. The benefits of quantum computing are colossal time and computational memory reduction with which the computational tasks are performed, which leads to the accuracy of the computations. This paper presents a comprehensive summary of quantum computing for traditional and Blockchain financial applications.
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.
This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services. We conclude with a summary of technical challenges and future prospects.
Quantum computing, even though it is still in its early stages, holds the potential to revolutionize the finance sector by providing efficient solutions to complex problems.This potential is evident in the direct correlation between key financial issues and the principles of quantum mechanics, such as the mathematical connection between the Black-Scholes-Merton formula and the Schrödinger equation.Nevertheless, quantum computing also brings about significant risks, notably threats to cybersecurity that could endanger systems like Blockchain.Financial institutions in the Netherlands, along with others worldwide, are beginning to contemplate these implications, making efforts to forecast the progress of quantum computing in order to adapt their operational strategies accordingly.This paper delves into the impact of quantum computing on finance, from the advantages in portfolio optimization, arbitrage, credit scoring, and fraud detection, to the challenges concerning the security of payment systems and the integrity of Blockchain.Additionally, potential solutions to these challenges are explored.The evolution of quantum computing necessitates a shift in focus towards less popular yet highly desirable applications that exhibit inherent complexity.These are the areas where quantum computers are anticipated to make a significant impact.
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 computing has been rapidly evolving as a field, with innovations driven by industry, academia, and government institutions. The technology has the potential to accelerate computation for solving complex problems across multiple industrial sectors. Finance and economics, with many problems exhibiting computationally heavy requirements, comprise a high-profile sector where quantum computing could have a significant impact. Therefore, it is important to identify and understand to what extent the technology could find utility in the sector. This technical review is written for quantum applications researchers, quantitative analysts in finance and economics, and researchers in related mathematical sciences. It is divided into two parts: (i) a survey of quantum algorithms pertinent to problems in finance and economics, and (ii) mapping of several use cases in the sector to the potential quantum algorithms presented in part (i). We discuss some challenges on the pathway to achieving quantum advantage. Ultimately, this review aims to be a catalyst for interdisciplinary research that will accelerate the advent of the practical advantages of quantum technologies to solve complex problems in this sector.
This review article discusses the application of quantum computing to financial problems while presenting current approaches and their future prospects. We also talk about quantum machine learning and deep learning in finance. In the banking industry, we look at the most recent developments and the state of the art in quantum computing. Following a quick introduction to financial derivatives, we go over the key models and techniques for estimating the effects of quantum computing. The most popular quantum financial algorithms and their quantum adversary are then described. Lastly, we discuss the main problems that must be solved in order for quantum algorithms to truly benefit the financial industry.