Report on 1812.09329v1
Matthew J. S. Beach, Isaac J. S. De Vlugt, Anna Golubeva, Patrick Huembeli, Bohdan Kulchytskyy, Xiuzhe Luo, Roger G. Melko, Ejaaz Merali, Giacomo Torlai · 2019
As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices.Computational techniques can be used to reconstruct a state from data, however the growing number of qubits demands ongoing algorithmic advances in order to keep pace with experiments.In this paper, we present an open-source software package called QuCumber that uses machine learning to reconstruct a quantum state consistent with a set of projective measurements.QuCumber uses a restricted Boltzmann machine to efficiently represent the quantum wavefunction for a large number of qubits.New measurements can be generated from the machine to obtain physical observables not easily accessible from the original data.