Accelerated Characterization of Two‐Level Systems in Superconducting Qubits Via Machine Learning

Q: What does this research mean for the field?

A custom convolutional neural network can extract two-level system parameters from simulated spectroscopy data with superior performance to traditional perturbation theory methods, maintaining high accuracy even in the presence of noise when appropriately trained. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Avinash Pathapati; Olli Mansikkamäki; Alexander C. Tyner; A. V. Balatsky

doi:10.1002/qute.202500868

March 1, 2026Advanced Quantum TechnologiesOpen Access

Accelerated Characterization of Two‐Level Systems in Superconducting Qubits Via Machine Learning

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Key Points

The aim is to develop a machine learning model for accurately extracting parameters of two-level systems from spectroscopy data.
Utilized a custom convolutional neural network (CNN) to analyze spectroscopy images.
Extracted parameters: frequency, coupling strength, dissipation time, and pure dephasing time.
Trained model initially on noise-free data and retrained on noisy data for improved accuracy.
The CNN model outperformed traditional perturbation theory methods in parameter extraction.
Retraining the model on noisy datasets significantly improved its performance and accuracy.
Higher predictive accuracy achieved for frequency and coupling strength parameters compared to dissipation and dephasing times.

Implication

Data-driven methods predict parameters of two-level systems in superconducting qubits, suggesting new approaches for characterization.

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Cite This Study

Pathapati et al. (2026) studied this question.

synapsesocial.com/papers/69c8c277de0f0f753b39cb64 https://doi.org/10.1002/qute.202500868

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Accelerated Characterization of Two‐Level Systems in Superconducting Qubits Via Machine Learning

Key Points

Implication

Discussion

Cite This Study

Accelerated Characterization of Two‐Level Systems in Superconducting Qubits Via Machine Learning

Key Points

Implication

Discussion

Cite This Study

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