Quantum Neural Estimation of Entropies

Understand

Entropy measures quantify the amount of information and correlation present in a quantum system.

• In practice, when the quantum state is unknown and only copies thereof are available, one must resort to the estimation of such entropy measures.
• Here we propose a variational quantum algorithm for estimating the von Neumann and R\'enyi entropies, as well as the measured relative entropy and measured R\'enyi relative entropy.
• Our approach first parameterizes a variational formula for the measure of interest by a quantum circuit and a classical neural network, and then optimizes the resulting objective over parameter space.