Neural-network fermionic states: precise measurements on quantum computers and variational Monte Carlo

by Antonio Mezzacapo (IBM research )

Tuesday 28 Jan 2020, 14:00 → 16:00 Europe/Rome

Aula Conversi (Dipartimento di Fisica - Ed. E.Fermi)

In the first part of this talk I will introduce neural-network
estimators for quantum observables, obtained by integrating the
measurement apparatus of a quantum simulator with neural networks.
Unsupervised learning of single-qubit measurement data can produce
estimates of complex observables free of quantum noise. Precise estimates
are achieved for quantum chemistry Hamiltonians, with a reduction of
several orders of magnitude in the amount of measurements needed compared
to standard estimators. I will show results on molecular systems obtained
using IBM superconducting quantum processors.
In the second part, I will show how the integration of quantum information
and machine learning techni