Contemporary quantum computers are noisy. For the correct operation of quantum software, it is essential to have an accurate and practical system that captures the actual noise behaviour. This is a significant Quantum Software Engineering (QSE) challenge as simulator noise models and manufacturers’ reported noise data are often inaccurate and out of sync with current hardware behaviour. Providing a practical solution is difficult because traditional noise reconstruction scales exponentially in the number of qubits and is likely to be outdated by the time it is obtained.

In this vision paper, we outline a novel pipeline to address the challenge by adapting classical shadow tomography from quantum mechanics. Our pipeline, called SHADOWP, enables empirical noise fingerprinting, avoiding the exponential number of measurements and allowing for a continuously updatable noise fingerprint in the software development pipeline, e.g. in a simulation environment. As a proof of concept, we implement the pipeline and report initial evaluation on two frameworks, Qiskit and Cirq, under two widely used hardware-informed profiles, IBM Boston and Quantinuum H2. Our results show fingerprints exhibit channel-specific structure, produce interpretable heatmaps, and reveal systematic cross-platform discrepancies under matched noise configurations, quantified by large Frobenius distances at a fraction of full tomography cost; on 69 MQTBench programs, larger fingerprint deltas correlate with cross-configuration output distributions divergence, signalling portability risk.