Quantum image classification using principal component analysis
arXiv:1504.00580v116 citations
AI Analysis
This work addresses image classification for quantum computing applications, but it appears incremental as it adapts classical PCA to a quantum context without reported performance gains.
The authors tackled image classification by developing a quantum algorithm that uses principal component analysis and von Neumann measurements, along with a new quantum representation for grayscale images.
We present a novel quantum algorithm for classification of images. The algorithm is constructed using principal component analysis and von Neuman quantum measurements. In order to apply the algorithm we present a new quantum representation of grayscale images.