arXiv:2109.04298 · International Conference on Computer Aided Design
Quantum Machine Learning for Finance ICCAD Special Session Paper
Marco Pistoia, Syed Farhan, Akshay Ajagekar, Alexander Buts, Shouvanik Chakrabarti, Dylan Herman, Shaohan Hu, Andrew Jena, Pierre Minssen, Pradeep Niroula, Arthur G. Rattew, Yue Sun, Romina Yalovetzky
Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive 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 review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.
Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive 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 review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.
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 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.
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.
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 (Figure 9), 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.
Quantum computing has the potential to revolutionize many industries, including banking. It is expected that by the end of this decade, quantum computers will have surpassed regular computers in processing capabilities, which will have far-reaching consequences for many sectors, including the financial sector. In particular, it is predicted that the financial industry will gain the most in the short, medium, and long term from using quantum computing. The authors of this chapter discuss the potential benefits of combining machine learning with quantum computing for use in financial modeling. Combining quantum computing and machine learning, or quantum machine learning (QML), provides novel approaches to solving difficult economic problems that classical computers are ill-equipped to tackle. The benefits of employing QML for risk management, credit rating, portfolio optimization, option pricing, and uncovering arbitrage opportunities are addressed, as well as an introduction to the principles of quantum computing. It also describes the quantum hardware and software and the potential challenges and ethical problems associated with QML’s use in the financial sector.
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.