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QuCumber: wavefunction reconstruction with neural networks

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

Submission summary

As Contributors: Roger Melko
Arxiv Link: https://arxiv.org/abs/1812.09329v1
Date submitted: 2019-01-02
Submitted by: Melko, Roger
Submitted to: SciPost Physics
Domain(s): Computational
Subject area: Quantum Physics

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.

Current status:
Editor-in-charge assigned

Submission & Refereeing History

Submission 1812.09329v1 (2 January 2019)

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