New paradigm for parameter estimation

NN

 

In our latest work, now on arXiv, we show how to use a convolutional neural network to extract physical parameters (even the quantum ones!) from experimental currents.

In my PhD I was generally concerned with monitoring and parameter estimation of quantum systems. These elements are crucial for efficiently functioning quantum devices, and, in difference from on-chip quantum operations, there is still a long way to go in terms of getting efficient readout at reasonable times. The ability to extract the maximum amount of information from an experimental record is therefore essential.

In practice, the experimental noise is sometimes so stubborn and viciously correlated that it may be really hard if not impossible to construct a quantum model that describes it. In our work we show that even for the cases where traditional parameter estimation methods do not work the convolutional network is a great solution to find the parameters governing the dynamics of the system.