Accreditation Against Limited Adversarial Noise

Abstract An accreditation protocol (a variety of quantum verification) is presented, where error is assumed to be adversarial (in contrast to the assumption error is implemented by identical CPTP maps used in previous accreditation protocols)—albeit slightly modified to reflect physically motivated error assumptions. This is achieved by upgrading a pre‐existing accreditation protocol (from [S. Ferracin et al. Phys. Rev. A 104, 042603 (2021)]) to function correctly in the face of adversarial error, with no diminution in efficiency or suitability for near‐term usage.

Authors

Institutions

Publication Details

Journal
Advanced Quantum Technologies
Published
2025-10-24
DOI
https://doi.org/10.1002/qute.202500337
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accreditation Against Limited Adversarial Noise

Andrew Jackson
Advanced Quantum Technologies
Adversarial Robustness in Machine Learning
article

Accreditation Against Limited Adversarial Noise

Andrew Jackson
article en

Abstract

Abstract An accreditation protocol (a variety of quantum verification) is presented, where error is assumed to be adversarial (in contrast to the assumption error is implemented by identical CPTP maps used in previous accreditation protocols)—albeit slightly modified to reflect physically motivated error assumptions. This is achieved by upgrading a pre‐existing accreditation protocol (from [S. Ferracin et al. Phys. Rev. A 104, 042603 (2021)]) to function correctly in the face of adversarial error, with no diminution in efficiency or suitability for near‐term usage.

Advanced Quantum TechnologiesVol. 9(2)
University of Warwick (GB), University of Edinburgh (GB)
Openalex Percentile: Top 84%
Adversarial Robustness in Machine Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.