Recently, FinTech has emerged as a hot topic, utilizing technology to enhance and innovate financial services and products. This expanding topic requires theoretical models and innovative methodologies to forward new business challenges. In today’s world, where vast amounts of data are generated every day, the need for computers capable of accurate predictive computations is becoming increasingly critical. Consequently, many financial institutions are turning to quantum computing, which promises to analyze large datasets and deliver results more quickly and accurately than any classical computer ever could. On the other hand, knowledge about quantum computing is not yet widely diffused in finance communities. In this work, after providing a gentle introduction to quantum mechanics, we review the state of the art of quantum computing in Fintech, touching such themes as stochastic modeling, optimization, and machine learning. Theoretical results, as well as practical solution, are discussed with the associated challenges.

A perspective on quantum Fintech

Ugo Fiore;Federica Gioia
;
Paolo Zanetti
2024-01-01

Abstract

Recently, FinTech has emerged as a hot topic, utilizing technology to enhance and innovate financial services and products. This expanding topic requires theoretical models and innovative methodologies to forward new business challenges. In today’s world, where vast amounts of data are generated every day, the need for computers capable of accurate predictive computations is becoming increasingly critical. Consequently, many financial institutions are turning to quantum computing, which promises to analyze large datasets and deliver results more quickly and accurately than any classical computer ever could. On the other hand, knowledge about quantum computing is not yet widely diffused in finance communities. In this work, after providing a gentle introduction to quantum mechanics, we review the state of the art of quantum computing in Fintech, touching such themes as stochastic modeling, optimization, and machine learning. Theoretical results, as well as practical solution, are discussed with the associated challenges.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/140118
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