Automatic Characterization of Fluxonium Superconducting Qubits Parameters with Deep Transfer Learning

• URL: http://arxiv.org/abs/2503.12099v1
• Date: Sat, 15 Mar 2025 11:51:16 GMT
• Title: Automatic Characterization of Fluxonium Superconducting Qubits Parameters with Deep Transfer Learning
• Authors: Huan-Hsuan Kung, Chen-Yu Liu, Qian-Rui Lee, Chiang-Yuan Hu, Yu-Chi Chang, Ching-Yeh Chen, Daw-Wei Wang, Yen-Hsiang Lin,
• Abstract summary: In solid state systems, the parameters of individual qubits vary across the entire system.<n>Recent developed superconducting qubits, such as fluxonium or 0-pi qubits, offer improved fidelity operations but exhibit a more complex physical and spectral structure.<n>We propose a machine learning (ML)based methodology for the automatic and accurate characterization of fluxonium qubit parameters.
• Score: 0.5932428354861273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate determination of qubit parameters is critical for the successful implementation of quantum information and computation applications. In solid state systems, the parameters of individual qubits vary across the entire system, requiring time consuming measurements and manual fitting processes for characterization. Recent developed superconducting qubits, such as fluxonium or 0-pi qubits, offer improved fidelity operations but exhibit a more complex physical and spectral structure, complicating parameter extraction. In this work, we propose a machine learning (ML)based methodology for the automatic and accurate characterization of fluxonium qubit parameters. Our approach utilized the energy spectrum calculated by a model Hamiltonian with various magnetic fields, as training data for the ML model. The output consists of the essential fluxonium qubit energy parameters, EJ, EC, and EL in Hamiltonian. The ML model achieves remarkable accuracy (with an average accuracy 95.6%) as an initial guess, enabling the development of an automatic fitting procedure for direct application to realistic experimental data. Moreover, we demonstrate that similar accuracy can be retrieved even when the input experimental spectrum is noisy or incomplete, highlighting the model robustness. These results suggest that our automated characterization method, based on a transfer learning approach, provides a reliable framework for future extensions to other superconducting qubits or different solid-state systems. Ultimately, we believe this methodology paves the way for the construction of large-scale quantum processors.

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