A comparative study of Quantum Image Representation techniques as data encoding in Variational Classifiers
conference paper
Quantum Image Representation represents a promising frontier in Machine Learning and image processing. It provides unique opportunities to revolutionize how classical images can be represented as a quantum state using a reasonable number of qubits. This research investigates three state-of-theart Quantum Image Representation techniques to determine their benefits in obtaining a quantum state representing the classical image. By implementing and comparing these techniques, we explore their potential for encoding classical images. Subsequently, the Variational Classifier is applied to investigate whether these encoding types offer advantages over traditional data loading functions. Despite observed efficiency gains, distinguishing significant advantages over traditional data loading techniques remains challenging.
Topics
TNO Identifier
1024231
Collation
23 p.
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