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Quantum Machine Learning

Quantum machine learning: Transforming cloud-based AI solutions

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Authors: Bangar Raju Cherukuri

Year

2020

Paper ID

12057

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

102

Citations

N/A

Abstract

This study examines the feasibility of placing quantum computing technology into cloud ML systems to make QML far faster and more scalable. Quantum computers tackle standard ML performance challenges through their special traits, including superposition and entanglement. Implementing QML on cloud-based platforms unlocks the specific advantages of scalability and accessibility while providing the required flexibility. Cloud-based systems can better predict results with faster performance when they use quantum algorithms to process machine learning tasks. This research examines how QML connects to cloud computing technology while showing how these industries can use it to handle limited processing power and improve overall system performance.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • This study examines the feasibility of placing quantum computing technology into cloud ML systems to make QML far faster and more scalable.

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References & Citation Signals

[1] DOI https://doi.org/10.30574/ijsra.2020.1.1.0041 [2] Publisher https://doi.org/10.30574/ijsra.2020.1.1.0041

Local Citation Graph (Related-Paper Links)

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