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Trapped Ion Quantum Computing Quantum Machine Learning

Measuring quantum entanglement, machine learning and wave function tomography: Bridging theory and experiment with the quantum gas microscope

arXiv
Authors: Norm M. Tubman

Year

2016

Paper ID

43338

Status

Preprint

Abstract Read

~2 min

Abstract Words

168

Citations

N/A

Abstract

There is an enormous amount of information that can be extracted from the data of a quantum gas microscope that has yet to be fully explored. The quantum gas microscope has been used to directly measure magnetic order, dynamic correlations, Pauli blocking, and many other physical phenomena in several recent groundbreaking experiments. However, the analysis of the data from a quantum gas microscope can be pushed much further, and when used in conjunction with theoretical constructs it is possible to measure virtually any observable of interest in a wide range of systems. We focus on how to measure quantum entanglement in large interacting quantum systems. In particular, we show that quantum gas microscopes can be used to measure the entanglement of interacting boson systems exactly, where previously it had been thought this was only possible for non-interacting systems. We consider algorithms that can work for large experimental data sets which are similar to theoretical variational Monte Carlo techniques, and more data limited sets using properties of correlation functions.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2016 reference point for readers tracking recent quantum research.
  • There is an enormous amount of information that can be extracted from the data of a quantum gas microscope that has yet to be fully explored.

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