Querying quantum computers with neural networks: precise measurements and noise reduction

Abstract

In 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. Finally, I will show results on molecular systems obtained using IBM superconducting quantum processors, combining precise measurements with error mitigation strategies.