Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm. This paper presents a comprehensive benchmarking study of classical and quantum machine learning models for image recognition on the MNIST handwritten digit dataset, evaluating both traditional models, a Classical Support Vector Machine (CSVM) and a Quantum Support Vector Machine (QSVM), and deep neural network models, a Classical Convolutional Neural Network (CCNN) and a Quantum Convolutional Neural Network (QCNN), across four performance dim...
| Authors | Sudip Vhaduri, Ryan Gammon, Sayanton Dibbo |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 19 July 2026 |
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