QuCumber: wavefunction reconstruction with neural networks

paper
package
Published

July 16, 2019

Info

QuCumber stands for “Quantum Calculator Used for Many-Body Eigenstate Reconstruction” (if you suspect we bent the full title to match the abbreviation, you are not wrong). This is a large team effort of the PIQuIL crew to put our know-how about quantum state reconstruction with RBMs into a neat user-friendly python package. Here is the github repo, here are the docs, and here is the accompanying paper published on SciPost.

Authors

Matthew J. S. Beach, Isaac De Vlugt, Anna Golubeva, Patrick Huembeli, Bohdan Kulchytskyy, Xiuzhe Luo, Roger G. Melko, Ejaaz Merali, Giacomo Torlai

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.