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Trapped Ion Quantum Computing

Detecting Markovianity of Quantum Processes via Recurrent Neural Networks

arXiv
Authors: Angela Rosy Morgillo, Massimiliano F. Sacchi, Chiara Macchiavello

Year

2024

Paper ID

66670

Status

Preprint

Abstract Read

~2 min

Abstract Words

85

Citations

N/A

Abstract

We present a novel methodology utilizing Recurrent Neural Networks (RNNs) to classify Markovian and non-Markovian quantum processes, leveraging time series data derived from Choi states. The model exhibits exceptional accuracy, surpassing 95%, across diverse scenarios, including dephasing and Pauli channels in an arbitrary basis, generalized amplitude damping dynamics, and even in the presence of noise. Additionally, the developed model shows efficient forecasting capabilities for the analyzed time series data. These results suggest the potential of RNNs in discerning and predicting the Markovian nature of quantum processes.

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  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
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  • We present a novel methodology utilizing Recurrent Neural Networks (RNNs) to classify Markovian and non-Markovian quantum processes, leveraging time series data derived from...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2406.07226 [2] arXiv https://arxiv.org/abs/2406.07226 [3] Publisher https://arxiv.org/abs/2406.07226

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