Shadow-Based Noise Fingerprinting of Simulated Quantum Noise Models
2026-07-10 • Software Engineering
Software Engineering
AI summaryⓘ
The authors address the challenge of identifying different types of noise in small quantum computers, which is important for keeping them working well. They created a method that uses special measurements and carefully chosen data features to tell apart different noise patterns from just a few test circuits. They tested three machine learning models and found that a random forest gave the best results, correctly identifying noise types about 84% of the time. Their analysis shows some noise types are easier to recognize than others, especially when noises have similar effects, suggesting future improvements are possible.
quantum noisequantum process tomographyclassical shadow tomographyPauli measurementsfeature engineeringrandom forest classifierquantum error characterizationmachine learningquantum processorsnoise channels
Authors
Vridhi Jain, Lei Zhang
Abstract
Accurate noise classification is essential for operating near-term quantum processors, yet existing approaches, such as quantum process tomography, scale exponentially with system size, limiting their practicality for routine calibration. We propose a scalable noise fingerprinting pipeline that combines structured classical shadow tomography with physics-informed feature engineering to identify noise channels from a fixed set of 3-qubit probe circuits. Each sample is represented by a 279-dimensional feature vector constructed from randomized Pauli measurements and derived observables, designed to resolve physically similar noise channels that produce overlapping signatures under generic measurement sets. We evaluate three classifiers, i.e., random forest, extra trees, and a multilayer perceptron, on a dataset of 14,000 labeled samples spanning 10 noise types. The random forest classifier achieves the highest test accuracy of 0.8426 with a macro F1 score of 0.8437, outperforming both baselines. Confusion analysis reveals that many noise types are classified with high reliability, with the remaining confusions occurring between channels sharing similar physical decay mechanisms, motivating future work on richer probe states and noise parameter estimation.