# Learning the noise fingerprint of quantum devices

arXiv: https://arxiv.org/abs/2109.11405 · HTML: https://mlsanity.com/paper/2109.11405/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Learning Non-Markovian Quantum Noise from Moir'e-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm](https://mlsanity.com/paper/1912.04368/) (2019)

## Built on by (most cited first, 12 of 12)

- [Quantum-Noise-Driven Generative Diffusion Models](https://mlsanity.com/paper/2308.12013/) (2023)
- [Experimental quantum pattern recognition in IBMQ and diamond NVs](https://mlsanity.com/paper/2205.00561/) (2022)
- [Deep learning enhanced noise spectroscopy of a spin qubit environment](https://mlsanity.com/paper/2301.05079/) (2023)
- [The role of data embedding in equivariant quantum convolutional neural networks](https://mlsanity.com/paper/2312.13250/) (2023)
- [Noise fingerprints in quantum computers: Machine learning software tools](https://mlsanity.com/paper/2202.04581/) (2022)
- [Machine-learning based noise characterization and correction on neutral atoms NISQ devices](https://mlsanity.com/paper/2306.15628/) (2023)
- [Machine-learning based high-bandwidth magnetic sensing](https://mlsanity.com/paper/2409.12820/) (2024)
- [Computing Graph Edit Distance with Algorithms on Quantum Devices](https://mlsanity.com/paper/2111.10183/) (2021)
- [Estimating the Effect of Crosstalk Error on Circuit Fidelity Using Noisy Intermediate-Scale Quantum Devices](https://mlsanity.com/paper/2402.06952/) (2024)
- [A short review on the maximum clique problem algorithms with classical, AI, and quantum methods](https://mlsanity.com/paper/2403.09742/) (2024)
- [Assumption-free fidelity bounds for hardware noise characterization](https://mlsanity.com/paper/2504.07010/) (2025)
- [Quantum Diffusion Models for Medical Image Analysis](https://mlsanity.com/paper/2609.31070/) (2026)

## Abstract (arXiv)

Noise sources unavoidably affect any quantum technological device. Noise's main features are expected to strictly depend on the physical platform on which the quantum device is realized, in the form of a distinguishable fingerprint. Noise sources are also expected to evolve and change over time. Here, we first identify and then characterize experimentally the noise fingerprint of IBM cloud-available quantum computers, by resorting to machine learning techniques designed to classify noise distributions using time-ordered sequences of measured outcome probabilities.