Quick Navigation

Overview Topics Abstract Importance Paper Tools References Reactions Discussion Publication Related

Topics

Quantum Machine Learning

Learning the noise fingerprint of quantum devices

arXiv

Authors: Stefano Martina, Lorenzo Buffoni, Stefano Gherardini, Filippo Caruso

Year

2021

Paper ID

61321

Status

Preprint

Abstract Read

~2 min

Abstract Words

Citations

N/A

Abstract

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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2021 reference point for readers tracking recent quantum research.
Noise sources unavoidably affect any quantum technological device.

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Become a member Sign in

Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

[1] DOI https://doi.org/arXiv:2109.11405 [2] arXiv https://arxiv.org/abs/2109.11405 [3] Publisher https://arxiv.org/abs/2109.11405

Local Citation Graph (Related-Paper Links)

External citation index: OpenAlex citation signal

Community Reactions

Quick sentiment from readers on this paper.

Score: 0

Likes: 0 Dislikes: 0

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.