Quantum finance: University of Naples and Intesa Sanpaolo successfully test a new algorithm.

Using quantum computing to address and solve complex financial problems more quickly and accurately, from developing advanced models for credit risk analysis (credit risk modeling) to implementing algorithms capable of predicting the price dynamics of derivative financial instruments (derivative pricing).

A group of researchers from Federico II University , Intesa Sanpaolo , and the startup G2Q Computing announced they have achieved significant results in this direction, taking a decisive step toward quantum finance . The researchers declared they achieved unprecedented performance in performing quantum Gaussian sampling (QGS). Quantum Gaussian sampling is a type of quantum algorithm used to simulate probability distributions with potential applications in a wide range of cutting-edge fields, such as quantum machine learning , cryptography , and, last but not least, quantum finance .

The results were obtained thanks to the use of Partenope , the 25-qubit superconducting quantum computer hosted at Federico II and funded by the ICSC (National Research Center in High Performance Computing, Big Data and Quantum Computing), a platform funded by the National Research Council (PNRR) that aims to promote the development of hardware and software solutions for quantum computing and the creation of an Italian supply chain dedicated to these technologies.

"The project," explains Davide Corbelletto , team leader of Intesa Sanpaolo 's Quantum Competence Center, "arose from a need at Intesa Sanpaolo, which had already attempted to run algorithms similar to QGS on other types of quantum computers, but encountered machine-level constraints that had previously been impossible to address directly. This problem," continues Corbelletto, "was solved thanks to a deep understanding of the operating mechanisms of the Partenope quantum computer processor and the algorithmic optimization skills of researchers at Federico II and G2Q Computing, which enabled us not only to generate the expected normal distributions, but more importantly, to control their characteristic parameters. This result," adds Corbelletto, "is particularly relevant for quantum finance, which aims to address potentially very complex problems, such as credit risk modeling or derivative pricing, more quickly and with greater accuracy." It also demonstrates how collaboration between the public and private sectors represents an added value for innovation and the development of concrete solutions in the quantum field."

Thanks to this new milestone, the Superconducting Quantum Computing Center at the University of Naples Federico II confirms its position as a strategic hub in the Italian quantum ecosystem and a successful example of an innovation model based on public-private collaboration.