Reconstructing quantum noise from symmetry-violating generators
Year: 2026
Type: article
Abstract: By systematically introducing small symmetry-violating terms into quantum gates, we examine whether such perturbations can reproduce the tomographic noise patterns observed on actual quantum hardware. This study fits into the broader context of quantum computing and quantum information processing, where realistic noise modeling remains essential for understanding device behavior.Our analytical framework mirrors Qiskit’s tomography procedures and shot model, using transparent noise control. Our results show that weak generator perturbations can reproduce average deviations in Pauli Transfer Matrices with high fidelity (given the recorded hardware noise characteristics). These findings suggest that symmetry-violating generators provide a phenomenological model for relevant classes of hardware noise. This approach offers a reproducible analytic baseline for testing noise hypotheses and calibrating simplified models against experimental data.Beyond this descriptive role, the appearance of stable symmetry-violating perturbations in the fitted models supports their physical relevance: they indicate that real devices operate slightly outside the symmetry-preserving regime assumed in standard noise models, and that these deviations leave identifiable signatures in tomographic reconstructions. Because the analytical framework isolates these violations at the generator level, it enables principled diagnosis of non-unitality and related effects. As a result, the model not only reproduces experimental process tomography data but also identifies which symmetry assumptions fail and by what magnitude, providing actionable information for calibration, simulation, and the development of symmetry-sensitive mitigation and error-correction strategies.
Source: APS Open Science
DOI: 10.1103/hg15-cjnx
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G2494861986
OpenAlex ID: W7203608634