Toward Live Noise Fingerprinting in Quantum Software Engineering

TL;DR

This paper introduces SIMSHADOW, a novel noise fingerprinting pipeline for quantum computers that enables efficient, up-to-date noise characterization to improve testing, debugging, and cross-platform compatibility.

Contribution

The paper presents a new classical shadow tomography-based method for noise fingerprinting in quantum software, addressing scalability and update challenges of traditional approaches.

Findings

Fingerprints reveal structured, interpretable noise patterns.

Cross-platform discrepancies are detectable with large Frobenius distances.

Larger fingerprint differences correlate with output distribution divergences.

Abstract

Contemporary quantum computers are inherently noisy, posing significant challenges for the development and testing of quantum software. Simplified or outdated noise assumptions can lead to incorrect assessments of program correctness, obscure debugging, and hinder cross-platform portability, creating a critical quantum software development gap. Providing accurate, practical noise characterisation is challenging as traditional reconstruction methods scale exponentially and rapidly become outdated. In this vision paper, we address this gap via a novel classical shadow tomography-based pipeline, SIMSHADOW, enabling efficient, continuously updatable noise fingerprinting from empirical observations, suitable for integration into quantum software development workflows, including testing and validation. We prototyped the pipeline to investigate fingerprints' ability to capture structured, interpretable noise and cross-platform discrepancies affecting quantum programs' behaviour to support realistic testing and debugging in future tools. Our evaluation with Qiskit and Cirq under widely used hardware-informed profiles, IBM Boston and Quantinuum H2, shows fingerprints exhibit channel-specific structure and yield interpretable heatmaps. We observed 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 differences correlate with output distributions divergences, highlighting threats for testing and cross-platform debugging tasks.