Automating detection of Two-Level Systems in Superconducting Qubits

• URL: http://arxiv.org/abs/2608.21983v1
• Date: Sat, 22 Aug 2026 14:32:16 GMT
• Title: Automating detection of Two-Level Systems in Superconducting Qubits
• Abstract summary: Two-level system (TLS) defects remain a primary mechanism of decoherence and operational instability in superconducting transmon qubits.<n>We present and benchmark two complementary analysis pipelines for extracting TLS statistics directly from time-resolved SWAP spectroscopy.
• Score: 29.039962620404822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Microscopic two-level system (TLS) defects remain a primary mechanism of decoherence and operational instability in superconducting transmon qubits, necessitating scalable and automated methods for their characterization. Here, we present and benchmark two complementary analysis pipelines for extracting TLS statistics directly from time-resolved SWAP spectroscopy: one-dimensional decay-rate fitting (1D-DRF), which detects defects via localized enhancements in the qubit relaxation rate, and a deterministic, non-parametric computer-vision framework (2D-CV) that achieves two-dimensional spectral localization by exploiting the temporal persistence of coherent population suppression. We deploy both methods on SWAP spectroscopy measurements from 52 flux-tunable transmon qubits on Rigetti processors with and without moderate ($\sim 10\%$) post-fabrication frequency trimming via Alternating-Bias Assisted Annealing (ABAA). We show that both pipelines converge on a consistent global characterization of the defect landscape while exhibiting complementary sensitivity across distinct coupling regimes. Crucially, both methods independently reveal a count--loss decoupling under moderate annealing: while the total detectable TLS defect density remains statistically unchanged, the span-integrated dielectric loss is reduced by approximately a factor of two, demonstrating selective suppression of the most strongly dissipative defect channels. These results establish an automated, non-parametric analysis framework for high-throughput hardware diagnostics and provide a statistical baseline for post-fabrication defect engineering in large-scale superconducting quantum processors.

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