Quantum Information metrics for Gaussian states

Quantum information metrics including fidelity, quantum divergences, and the trace norm provide the mathematical foundation for studying and understanding quantum states and processes. As such, quantum information metrics play a crucial role in quantum computing, simulation of quantum systems, and quantum machine learning (including quantum clustering, generative models, and machine learning of, and by, quantum states). Unfortunately, these metrics are limited: they are highly sensitive to experimental noise and phase differences. In the context of quantum machine learning, conventional metrics often suffer from vanishing gradients, barren plateaus, and poor local minima in the loss landscape.

This project focus on to develop the mathematical, physics and computational aspects for a novel class of Quantum Information metrics based on Optimal Transport focus on Gaussian states.

Quantum Gaussian states are fundamental in the field of quantum optics and quantum information theory. In the context of continuous-variable quantum systems, such as electromagnetic fields, quantum Gaussian states play a crucial role in various quantum communication and computation tasks. They possess unique properties, such as being stable under linear operations and easily manipulated by linear optical elements.

Faculty Supervisor:

Augusto Gerolin

Student:

Partner:

Universidade Estadual de Londrina

Discipline:

Mathematics

Sector:

Quantum Science; Artificial Intelligence

University:

University of Ottawa

Program: