Shadow-Based Noise Fingerprinting of Simulated Quantum Noise Models

2026-07-13 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Expert, long

Summary

A new scalable noise fingerprinting pipeline has been developed to classify quantum noise channels in near-term quantum processors. This pipeline combines structured classical shadow tomography with physics-informed feature engineering, utilizing 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, specifically designed to distinguish physically similar noise channels. The method was evaluated on a dataset of 14,000 labeled samples spanning 10 distinct noise types, including depolarizing, thermal relaxation, and readout error. Three classifiers were benchmarked: random forest, extra trees, and a multilayer perceptron. The random forest classifier achieved the highest test accuracy of 0.8426 with a macro F1 score of 0.8437, significantly outperforming the MLP baseline. Confusion analysis revealed high reliability for many noise types, with remaining confusions occurring between channels sharing similar physical decay mechanisms.

Key takeaway

For quantum engineers and researchers developing or calibrating near-term quantum processors, this shadow-based noise fingerprinting pipeline offers a scalable alternative to exponential-scaling tomography methods. You should consider implementing physics-informed feature engineering with classical shadow measurements to effectively distinguish between various noise channels. Prioritize ensemble tree classifiers like random forest, which achieved 0.8426 accuracy, over neural networks for robust and efficient noise identification in 3-qubit systems, especially when dealing with physically similar error mechanisms.

Key insights

Scalable quantum noise fingerprinting leverages classical shadows and physics-informed features for efficient classification.

Principles

• Classical shadow tomography enables scalable quantum state property estimation.
• Physics-informed features are critical for resolving similar quantum noise channels.
• Ensemble tree classifiers can outperform neural networks on structured quantum features.

Method

Prepare 3-qubit probe states, perform randomized Pauli shadow measurements, extract 279-dimensional physics-informed features, then classify noise types using ensemble ML models.

In practice

• Employ 3-qubit QAOA and simple states for comprehensive noise probing.
• Derive features from Pauli expectation values and coherence/population metrics.
• Favor random forest or extra trees for quantum noise classification.

Topics

• Quantum Noise Classification
• Classical Shadow Tomography
• Machine Learning
• Quantum Error Mitigation
• Qiskit
• Random Forest

Code references

• AdaHgrace/Noise_Fingerprinting

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.