Dr. Antonio Policicchio

Quantum-inspired tensor networks applied to computational-intensive financial problems.

In recent years, the financial industry has seen a growing interest in leveraging advanced computational techniques to solve complex problems. Among these, Quantum Information Theory offers promising tools that can significantly enhance our computational capabilities. This talk will explore the application of quantum-inspired tensor networks in addressing financial problems. Tensor networks, originally developed in the context of quantum many-body physics, provide a powerful framework for representing and manipulating large-scale data sets efficiently. By adapting these techniques, we can tackle various financial challenges, such as portfolio optimization, risk management, and option pricing, with improved accuracy and computational efficiency. This presentation will illustrate tensor network practical implementation in financial contexts, and showcase empirical results demonstrating their potential in financial analytics.