Gloria Turati is a PhD student in Information Technology at the Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) of Politecnico di Milano, under the supervision of Prof. Paolo Cremonesi. She received her Master’s degree in Mathematics with a score of 110/110 cum laude from Università degli Studi di Milano Bicocca. Following her graduation, she gained valuable experience in data analytics while working at Target Reply. Driven by a passion for computer science and innovative technologies, she began her PhD in Applied Quantum Machine Learning in November 2021. Her research focuses on Variational Quantum Algorithms (VQAs), aiming to understand their strengths, limitations, and potential applications. Her current focus is on constructing effective circuits for these algorithms. Notable projects include:

•  An application of the Quantum Approximate Optimization Algorithm (QAOA) to a Feature Selection problem formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem (presented at QCE '22 poster session).
•  A benchmarking study of various adaptive variational algorithms, where the circuit is designed dynamically during the algorithm's execution (presented at QCE '23).
• An investigation of the Variational Quantum Linear Solver (VQLS) for solving linear systems of equations and its application to fluid dynamics problems (in collaboration with Eni).
• The development of an algorithm that utilizes Reinforcement Learning to design circuits tailored to solving specific problems.

Through her research, Gloria aims to leverage the capabilities of the currently available quantum hardware to address real-world challenges.