Santosh Gundu@SungodLab

The Problem Quantum computing simulations depend on noise models to be realistic. A noise model is the mathematical description of how real hardware degrades a quantum circuit — gate errors, decoherence, readout mistakes. When a developer edits that noise model, even a small change can make the simulation silently less accurate. No error is thrown. No test fails. The code still runs. But the fidelity — how closely the simulation matches ideal quantum behavior — has quietly dropped. This is the silent regression problem. It is invisible to standard software testing because the simulation produces output either way. The only way to catch it is to measure how good that output actually is — and compare it against a known-good baseline. Without automation, that check simply does not happen, especially under deadline pressure. The Solution NoiseGuard is a lightweight, automated fidelity regression detector for quantum noise models. It wires directly into a developer's git workflow via a pre-commit hook. Every time a developer attempts to commit a change to the noise model, NoiseGuard automatically runs three benchmark quantum circuits through the updated model, measures the fidelity of each output against the mathematically ideal result, and compares those scores against a stored baseline. If any circuit's fidelity drops past a configurable threshold, the commit is blocked with a clear, actionable error message. The tool applies two layers of error mitigation before computing fidelity. Measurement Error Mitigation (MEM) is applied to every circuit by default — it corrects readout errors using a calibration matrix, making fidelity scores stable and reliable rather than contaminated by measurement device imperfection. Probabilistic Error Cancellation (PEC) is available as an opt-in second layer for circuits where gate-level error suppression is required, using Richardson extrapolation to estimate zero-noise behavior. The result is a side-by-side report showing raw fidelity, MEM-corrected fidelity, and deltas against baseline for every benchmark circuit — giving the developer immediate visibility into exactly what changed and by how much. Target Users NoiseGuard is built for quantum software developers and researchers who write or maintain noise models for Qiskit-based simulators. It is especially valuable in team settings, CI pipelines, or rapid-iteration hackathon environments where noise model changes are frequent and manual verification is impractical. How They Interact With It Interaction is zero-friction. The developer edits their noise model as normal and runs git commit. NoiseGuard runs silently in the background. If everything is fine, the commit goes through. If fidelity has regressed, the commit is blocked with a printed table showing exactly which circuit failed, by how much, and what threshold was exceeded. One command — python -m noiseguard.guard --update-baseline — reseeds the baseline when a noise model change is intentional. What Makes It Unique Most quantum testing tools focus on circuit correctness — did the circuit produce the right state? NoiseGuard focuses on model fidelity — is the simulation still realistic? That is a different and largely unaddressed problem. By combining git-native automation, two-layer error mitigation, and per-circuit configurable thresholds into a single sub-second command, NoiseGuard makes a previously invisible class of regression immediately visible — before it ever reaches a collaborator, a paper, or a production pipeline.

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