  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.
