# QuCumber: wavefunction reconstruction with neural networks

## TL;DR

QuCumber is an open-source machine learning tool that reconstructs quantum states from measurement data using neural networks, enabling efficient analysis of large qubit systems and generation of new measurements.

## Contribution

The paper introduces QuCumber, a novel software package employing restricted Boltzmann machines for quantum wavefunction reconstruction from experimental data.

## Key findings

- Successfully reconstructs quantum states from measurement data.
- Generates new measurements to access additional physical observables.
- Scales efficiently to large qubit systems.

## Abstract

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.