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

ANTONIO MEZZACAPO (IBM T.J. Watson Research Center, Yorktown Heights, NY, USA)   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 techniques allows to improve on classical coupled-cluster methods for some diatomic molecules.