Topological Mitigation of 1/f Decoherence in Superconducting Qubits via Causal-AI Taylor Sensitivity Operators on Real QPU Hardware
Year: 2026
Type: preprint
Abstract: This paper presents the first experimental validation of Causal-AI topological control on real superconducting quantum hardware. Addressing the fundamental bottleneck of ambient $1/f$ flicker noise and dephasing in quantum processors, we operationalize the Entropic-Resistance framework—modeling spacetime as a discrete network and time as a recursive relaxation process—to optimize transmon qubit performance. By extracting deterministic Taylor sensitivity operators ($j_1, j_2$) from real-time calibration data of IBM Quantum’s live ibm_fez processor, we compute and execute an analytically adjusted "Lasky-pulse" amplitude. Comparative hardware execution against standard factory baselines (validated across live QPU job execution logs: da294biein7c73bea68g and da29qpkdedkc73erutlg) demonstrates that topological network optimization successfully bridges discrete recursive spacetime theory with operational quantum control without requiring hardware modifications.
Author: Németh, László János
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7203698499
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