Eigenstate extraction with neural-network tomography

Understand

We discuss quantum state tomography via a stepwise reconstruction of the eigenstates of the mixed states produced in experiments.

• Our method is tailored to the experimentally relevant class of nearly pure states or simple mixed states, which exhibit dominant eigenstates and thus lend themselves to low-rank approximations.
• The developed scheme is applicable to any pure-state tomography method, promoting it to mixed-state tomography.
• Here, we demonstrate it with machine learning-inspired pure-state tomography based on neural-network representations of quantum states.