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Trapped Ion Quantum Computing Quantum Machine Learning

Exploring quantum localization with machine learning

arXiv
Authors: J. Montes, Lenoardo Ermann, Alejandro M. F. Rivas, Florentino Borondo, Gabriel G. Carlo

Year

2024

Paper ID

67031

Status

Preprint

Abstract Read

~2 min

Abstract Words

99

Citations

N/A

Abstract

We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom 'quantum' NN, with the pattern recognition capabilities of a modified convolutional model. This design accepts wave functions of any dimension as inputs and makes accurate predictions at an affordable computational cost. This scalability becomes crucial to explore the localization rate at the semiclassical limit, a long standing question in the quantum scattering field. Moreover, the physical meaning built in the model allows for the interpretation of the learning process

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 introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2406.00363 [2] arXiv https://arxiv.org/abs/2406.00363 [3] Publisher https://arxiv.org/abs/2406.00363

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