Summary of Adiabatic Quantum Support Vector Machines, by Prasanna Date et al.
Adiabatic Quantum Support Vector Machines
by Prasanna Date, Dong Jun Woun, Kathleen Hamilton, Eduardo A. Coello Perez, Mayanka Chandra Shekhar, Francisco Rios, John Gounley, In-Saeng Suh, Travis Humble, Georgia Tourassi
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantum Physics (quant-ph); Machine Learning (stat.ML)
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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 This paper proposes an adiabatic quantum computing approach to train support vector machines, leveraging their ability to solve complex optimization problems efficiently. The authors demonstrate a significant time complexity advantage, showcasing the potential for quantum computers to outperform classical ones in machine learning tasks. Across five benchmark datasets (Iris, Wisconsin Breast Cancer, Wine, Digits, and Lambeq), the proposed method achieves comparable test accuracy with classical approaches using Scikit-learn. Moreover, scalability studies reveal a 3.5-4.5 times speedup for the quantum approach when dealing with large datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called adiabatic quantum computers to help train machine learning models, like support vector machines. These computers are good at solving hard problems quickly, and this paper shows that they can be used to train models just as well as regular computers. The authors tested their approach on five different datasets and found that it worked just as well as the regular way of doing things. They also looked at how well the approach would work with very large datasets and found that it was much faster than regular computers. |
Keywords
* Artificial intelligence * Machine learning * Optimization
