We present a novel quantum algorithm for the 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
A flexible representation of quantum images (FRQI) was proposed to facilitate the extension of class...
The need to increase the complexity of computational methods to produce improvements in functional p...
Quantum computing holds great promise for a number of fields including biology and medicine. A major...
We present a novel quantum algorithm for the classification of images. The algorithm is constructed ...
Face recognition is one of the most ubiquitous examples of pattern recognition in machine learning, ...
Histogram plays an important statistical role in digital image processing. However, the existing qua...
Component Labeling, as a fundamental preprocessing task in image understanding and pattern recogniti...
Quantum machine learning, an important element of quantum computing, recently has gained research at...
Processing of digital images is continuously gaining in volume and relevance, with concomitant deman...
This paper narrows the gap between previous literature on quantum linear algebra and practical data ...
Quantum Machine learning is a promising technology that is related to the study of computing. Due to...
Quantum-based classifiers and architecture are gaining lots of attention in image representation and...
The classical image segmentation algorithm based on grayscale morphology can effectively segment ima...
Abstract Classical computing has borne witness to the development of machine learning. The integrati...
This paper proposes a new algorithm for binarizing the grayscale quantum images represented by novel...
A flexible representation of quantum images (FRQI) was proposed to facilitate the extension of class...
The need to increase the complexity of computational methods to produce improvements in functional p...
Quantum computing holds great promise for a number of fields including biology and medicine. A major...
We present a novel quantum algorithm for the classification of images. The algorithm is constructed ...
Face recognition is one of the most ubiquitous examples of pattern recognition in machine learning, ...
Histogram plays an important statistical role in digital image processing. However, the existing qua...
Component Labeling, as a fundamental preprocessing task in image understanding and pattern recogniti...
Quantum machine learning, an important element of quantum computing, recently has gained research at...
Processing of digital images is continuously gaining in volume and relevance, with concomitant deman...
This paper narrows the gap between previous literature on quantum linear algebra and practical data ...
Quantum Machine learning is a promising technology that is related to the study of computing. Due to...
Quantum-based classifiers and architecture are gaining lots of attention in image representation and...
The classical image segmentation algorithm based on grayscale morphology can effectively segment ima...
Abstract Classical computing has borne witness to the development of machine learning. The integrati...
This paper proposes a new algorithm for binarizing the grayscale quantum images represented by novel...
A flexible representation of quantum images (FRQI) was proposed to facilitate the extension of class...
The need to increase the complexity of computational methods to produce improvements in functional p...
Quantum computing holds great promise for a number of fields including biology and medicine. A major...
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