# Noise fingerprints in quantum computers: Machine learning software tools

arXiv: https://arxiv.org/abs/2202.04581 · HTML: https://mlsanity.com/paper/2202.04581/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Learning the noise fingerprint of quantum devices](https://mlsanity.com/paper/2109.11405/) (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, 3 of 3)

- [Deep learning enhanced noise spectroscopy of a spin qubit environment](https://mlsanity.com/paper/2301.05079/) (2023)
- [A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers](https://mlsanity.com/paper/2404.12892/) (2024)
- [Quantum Diffusion Models for Medical Image Analysis](https://mlsanity.com/paper/2609.31070/) (2026)

## Abstract (arXiv)

In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum noise sources affecting a quantum device, as a quantum computer. Specifically, the software architecture is designed to classify successfully (more than 99% of accuracy) the noise fingerprints in different quantum devices with similar technical specifications, or distinct time-dependences of a noise fingerprint in single quantum machines.