Machine-learning based noise characterization and correction on neutral
  atoms NISQ devices
Ettore Canonici Stefano Martina Riccardo Mengoni Daniele Ottaviani Filippo Caruso
Neutral atoms devices represent a promising technology that uses optical
tweezers to geometrically arrange atoms and modulated laser pulses to control
the quantum states. A neutral atoms Noisy Intermediate Scale Quantum (NISQ)
device is developed by Pasqal with rubidium atoms that will allow to work with
up to 100 qubits. All NISQ devices are affected by noise that have an impact on
the computations results. Therefore it is important to better understand and
characterize the noise sources and possibly to correct them. Here, two
approaches are proposed to characterize and correct noise parameters on neutral
atoms NISQ devices. In particular the focus is on Pasqal devices and Machine
Learning (ML) techniques are adopted to pursue those objectives. To
characterize the noise parameters, several ML models are trained, using as
input only the measurements of the final quantum state of the atoms, to predict
laser intensity fluctuation and waist, temperature and false positive and
negative measurement rate. Moreover, an analysis is provided with the scaling
on the number of atoms in the system and on the number of measurements used as
input. Also, we compare on real data the values predicted with ML with the a
priori estimated parameters. Finally, a Reinforcement Learning (RL) framework
is employed to design a pulse in order to correct the effect of the noise in
the measurements. It is expected that the analysis performed in this work will
be useful for a better understanding of the quantum dynamic in neutral atoms
devices and for the widespread adoption of this class of NISQ devices.