Physics informed neural networks learning a two-qubit Hamiltonian
Machine learning techniques are employed to perform the full characterization
of a quantum system. The particular artificial intelligence technique used to
learn the Hamiltonian is called physics informed neural network (PINN). The
idea behind PINN is the universal approximation theorem, which claims that any
function can be approximate by a neural network if it contains enough
complexity. Consequently, a neural network can be a solution of a physical
model. Moreover, by means of extra data provided by the user, intrinsic
physical parameters can be extracted from the approach called inverse-PINN.
Here, we apply inverse-PINN with the goal of extracting all the physical
parameters that constitutes a two qubit Hamiltonian. We find that this approach
is very efficient. To probe the robustness of the inverse-PINN to learn the
Hamiltonian of a two-qubit system, we use the IBM quantum computers as
experimental platforms to obtain the data that is plugged in the PINN. We found
that our method is able to predict the two-qubit parameters with 5% of accuracy
on average.
Leonardo K. Castelano Iann Cunha Fabricio S. Luiz Marcelo V. de Souza Prado Felipe F. Fanchini