QuCumber: Wavefunction Reconstruction with Neural Networks

As the field of quantum technology continues to evolve, accurate quantum state preparation is becoming increasingly important for various applications in materials, matter, and devices. Computational methods allow the reconstruction of quantum states from experimental data, but the increasing complexity of quantum systems demands continuous algorithmic innovations. In this paper, we introduce QuCumber, an open-source software package designed to reconstruct quantum states using machine learning techniques. QuCumber leverages restricted Boltzmann machines (RBMs) to represent quantum wavefunctions based on projective measurements. By utilizing RBMs, QuCumber efficiently generates new measurements that facilitate the calculation of physical observables, some of which may not be directly accessible through experimental data alone. This paper demonstrates QuCumber’s capability to handle both real and complex wavefunctions, providing valuable tools for advancing quantum state analysis in line with the growing scale of quantum systems.