Summary of Evaluating the Impact Of Different Quantum Kernels on the Classification Performance Of Support Vector Machine Algorithm: a Medical Dataset Application, by Emine Akpinar et al.
Evaluating the Impact of Different Quantum Kernels on the Classification Performance of Support Vector Machine Algorithm: A Medical Dataset Application
by Emine Akpinar, Sardar M. N. Islam, Murat Oduncuoglu
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantum Physics (quant-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the integration of classical data with a quantum machine learning technique called Support Vector Machine Algorithm with Quantum Kernel Estimator (QSVM-Kernel). The authors examine the effects of various feature mapping methods on classification results, using medical data analysis as a case study. They apply the QSVM-Kernel method to two publicly available medical datasets and analyze the impact of different quantum feature maps on classifier performance and execution time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to combine classical and quantum data in machine learning. It uses a special type of algorithm called QSVM-Kernel, which is designed to work with quantum computers. The researchers tested this algorithm on two medical datasets to see how it performed using different ways of preparing the data for the quantum computer. They found that certain methods worked better than others. |
Keywords
» Artificial intelligence » Classification » Machine learning » Support vector machine
