Summary of An Independent Implementation Of Quantum Machine Learning Algorithms in Qiskit For Genomic Data, by Navneet Singh and Shiva Raj Pokhrel
An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data
by Navneet Singh, Shiva Raj Pokhrel
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 application of Quantum Machine Learning algorithms, such as Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), and Quantum Neural Networks (QNN) in Qiskit, to classify genomic sequences. The authors evaluate various feature mapping techniques for these algorithms, demonstrating their effectiveness in this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses quantum machine learning to help scientists analyze genetic data more accurately. It compares different methods for using quantum computers to classify genetic information and shows that some of these methods work better than others. This research could be important for discovering new medicines or understanding how genes affect our health. |
Keywords
» Artificial intelligence » Machine learning
