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Random telegraph signal analysis with a recurrent neural network.

N J Lambert^1,2, A A Esmail³, M Edwards³

¹Department of Physics, University of Otago, Dunedin 9016, New Zealand.

Physical Review. E

|August 16, 2020

Summary

This summary is machine-generated.

We developed an artificial neural network to analyze noisy random telegraph signals and extract transition rates. This method accurately measures quasiparticle dynamics in superconducting devices, even with limited data.

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Area of Science:

Quantum Computing
Artificial Intelligence
Condensed Matter Physics

Background:

Random telegraph signals (RTS) are crucial for characterizing noise in quantum devices.
Extracting transition rates from noisy RTS is challenging, especially with limited bandwidth.
Current methods struggle with low signal-to-noise ratios.

Purpose of the Study:

To develop a robust method for analyzing asymmetric noisy RTS.
To extract underlying transition rates from complex signal data.
To apply the developed method to quasiparticle dynamics in superconducting circuits.

Main Methods:

Utilized an artificial neural network, specifically a long short-term memory (LSTM) network.
Trained the LSTM network on asymmetric noisy random telegraph signals.
Validated the method's performance against traditional techniques.

Main Results:

The LSTM network demonstrated superior performance in extracting transition rates compared to other methods.
The technique provides reliable results even when the signal-to-noise ratio approaches one.
The method is effective over a wide range of underlying transition rates.

Conclusions:

LSTM networks offer a powerful tool for analyzing noisy RTS in quantum systems.
This approach enhances the measurement of quasiparticle dynamics in superconducting double dots.
The method enables studies of quasiparticle dynamics across new temperature ranges.