• Corpus ID: 229158635

Practical application improvement to Quantum SVM: theory to practice

@article{Park2020PracticalAI,
  title={Practical application improvement to Quantum SVM: theory to practice},
  author={Jae-eun Park and Brian Quanz and Stephen P Wood and Heather Higgins and Ray Harishankar},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.07725}
}
Quantum machine learning (QML) has emerged as an important area for Quantum applications, although useful QML applications would require many qubits. Therefore our paper is aimed at exploring the successful application of the Quantum Support Vector Machine (QSVM) algorithm while balancing several practical and technical considerations under the Noisy Intermediate-Scale Quantum (NISQ) assumption. For the quantum SVM under NISQ, we use quantum feature maps to translate data into quantum states… 
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19 Citations
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Methods Citations
9

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19 Citations

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