Quantum computing, with its potential for large computational strength and potential to clear up complicated issues greater efficiently, has garnered big attention throughout various industries. One sector that stands to benefit greatly from quantum computing is the economic enterprise. The abilities provided using quantum computer systems can revolutionize several regions inside finance, such as chance evaluation, optimization, encryption, and portfolio management. One of the key packages of quantum computing in finance is hazard analysis. Financial establishments deal with huge quantities of records and complex models while assessing chance.
Quantum computing, with its potential for large computational strength and potential to clear up complicated issues greater efficiently, has garnered big attention throughout various industries. One sector that stands to benefit greatly from quantum computing is the economic enterprise. The abilities provided using quantum computer systems can revolutionize several regions inside finance, such as chance evaluation, optimization, encryption, and portfolio management. One of the key packages of quantum computing in finance is hazard analysis. Financial establishments deal with huge quantities of records and complex models while assessing chance.
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 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 computers can solve specific problems that are not feasible on "classical" hardware. Harvesting the speed-up provided by quantum computers therefore has the potential to change any industry which uses computation, including finance. First quantum applications for the financial industry involving optimization, simulation, and machine learning problems have already been proposed and applied to use cases such as portfolio management, risk management, and pricing derivatives. This survey reviews platforms, algorithms, methodologies, and use cases of quantum computing for various applications in finance in a structured way. It is aimed at people working in the financial industry and serves to gain an overview of the current development and capabilities and understand the potential of quantum computing in the financial industry.
Quantum computing is promised to have the potential to revolutionize many industries, providing faster and more accurate solutions to complex problems such as financial sector examples portfolio management, wealth management, risk analysis, and market predictions. However, the adoption of quantum computing faces several challenges such as technological limitations, integration with current IT systems, and potential risks to encryption methods. In this paper, we review recent research on quantum computing, discussing potential use cases, benefits, risks, and issues. We also highlight the need for further research to address these challenges before widespread adoption of quantum computing can occur. As financial sector appears to present most promising results it is considered as a key topic of this article.
The rapid advancement of quantum computing is poised to revolutionize industries, particularly in areas requiring complex computational power. This paper explores the potential impact of quantum computing on financial risk management from a business perspective. Traditional risk management models struggle to cope with the increasing complexity of global financial systems, making quantum computing a promising solution. By leveraging quantum algorithms, financial institutions can optimize risk assessment, enhance portfolio management, and significantly improve predictive models. This paper examines key algorithms and their applications in finance, discusses the challenges and opportunities in adopting quantum solutions, and presents case studies of early adopters. The findings suggest that quantum computing can offer a competitive advantage to businesses by providing superior risk analysis capabilities. However, limitations in technology, infrastructure, and expertise remain significant hurdles. This study contributes to the growing discourse on quantum computing by providing actionable insights for businesses looking to integrate quantum solutions into their risk management strategies.
In terms of computing capacity, quantum computers are expected to surpass conventional computers throughout this decade and have a seismic impact on a number of commercial sectors, particularly finance. In actuality, it is anticipated that the financial industry would reap both immediate and long-term benefits from quantum computing. The current state of quantum computing for financial applications is thoroughly evaluated in this survey research, with a focus on stochastic modelling, optimisation, and machine learning. It demonstrates how these methods could aid in more rapidly and efficiently resolving financial issues including derivative pricing, risk modelling and fraud detection if they were updated to operate on a quantum computer.The possibility of quantum computing as a ground-breaking tool for streamlining financial engineering procedures is examined in this research. Growing interest in using quantum computing to solve these problems is being driven by the complexity of financial systems and the computational rigor of financial engineering problems. The effectiveness of quantum computing in advancing optimization techniques in the field of financial engineering is assessed in this paper.