Our new paper concerning “Machine-Learning-Assisted Manipulation and Readout of Molecular Spin Qubits” is now online on Physical Review Applied at the following link.

Experimental workflow with the main steps of our methods, from
data acquisition to signal processing and to the execution of the
final task (amplitude or phase recognition).

“Machine learning finds application in the quantum control and readout of qubits. In this work we apply artificial neural networks to assist the manipulation and the readout of a prototypical molecular spin qubit-an oxovanadium(IV) moiety-in two experiments designed to test the amplitude and the phase recognition, respectively. We first successfully use an artificial network to analyze the output of a storage-retrieval protocol with four input pulses to recognize the echo positions and, with further post selection on the results, to infer the initial input pulse sequence. We then apply an artificial neural network to ascertain the phase of the experimentally measured Hahn echo, showing that it is possible to correctly detect its phase and to recognize additional single-pulse phase shifts added during manipulation.”

The photo below shows a gadget of Physical Review Applied collected by C. Bonizzoni during the APS March Meeting 2017 in New Orleans, Louisiana, USA (Impact Factor of Physical Review Applied was 4.06 after three years from its launch).

NEW PAPER!! – Machine-Learning-Assisted Manipulation and Readout of Molecular Spin Qubits