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Trapped Ion Quantum Computing Quantum Machine Learning

Representing arbitrary ground states of toric code by a restricted Boltzmann machine

arXiv
Authors: Penghua Chen, Bowen Yan, Shawn X. Cui

Year

2024

Paper ID

65896

Status

Preprint

Abstract Read

~2 min

Abstract Words

102

Citations

N/A

Abstract

We systematically analyze the representability of toric code ground states by Restricted Boltzmann Machine with only local connections between hidden and visible neurons. This analysis is pivotal for evaluating the model's capability to represent diverse ground states, thus enhancing our understanding of its strengths and weaknesses. Subsequently, we modify the Restricted Boltzmann Machine to accommodate arbitrary ground states by introducing essential non-local connections efficiently. The new model is not only analytically solvable but also demonstrates efficient and accurate performance when solved using machine learning techniques. Then we generalize our the model from Z2 to Zn toric code and discuss future directions.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • We systematically analyze the representability of toric code ground states by Restricted Boltzmann Machine with only local connections between hidden and visible neurons.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2407.01451 [2] arXiv https://arxiv.org/abs/2407.01451 [3] Publisher https://arxiv.org/abs/2407.01451

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