Quantum Machine Learning: Applications and Current Limitations

Quantum Machine Learning (QML) is an emerging field combining quantum computing and machine learning. This paper reviews current QML applications, highlights recent advancements, and discusses the limitations and challenges faced in practical implementations. Key areas include quantum-enhanced algorithms, hybrid models, and potential for accelerated data processing in AI.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-16
DOI
https://doi.org/10.5281/zenodo.16886926
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.00
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article

Quantum Machine Learning: Applications and Current Limitations

Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture
article

Quantum Machine Learning: Applications and Current Limitations

article en

Abstract

Quantum Machine Learning (QML) is an emerging field combining quantum computing and machine learning. This paper reviews current QML applications, highlights recent advancements, and discusses the limitations and challenges faced in practical implementations. Key areas include quantum-enhanced algorithms, hybrid models, and potential for accelerated data processing in AI.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 77%
Quantum Computing Algorithms and Architecture
DOIOpen Access PDFOpenAlex
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Quantum Machine Learning: Applications and Current Limitations · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS