Institute for Quantum Information Seminar
Taking a more targeted approach, we have developed schemes that enable (i) estimating the fidelity of an experiment to a theoretical ideal description , (ii) learning which description within a variational class of states best matches the experimental data [2-3].
In this talk, I will focus on task (ii), which we call variational tomography. I will present methods for identifying a state inside interesting variational classes such as matrix product states (MPS) , and multi-scale entanglement renormalisation ansatz (MERA) . For MERA, I will describe how to learn a state from a small number of efficiently-implementable measurements and fast post-processing, without requiring unitary control.
 da Silva, L.-C. and Poulin, PRL 107, 210404 (2011).  Cramer, Plenio, Flammia, Somma, Gross, Bartlett, L.-C., Poulin, Liu, Nature Commun. 1, 149 (2010).  L.-C., Poulin, arXiv:1204.0792.