الفهرس 
English SummaryOnly 14 pages are availabe for public view
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Abstract
The problem of pattern classification in quantum data has
been of great importance over the past few years. This
study investigates the effect of deploying Grover’s, the
partial diffusion, and the fixed-phase algorithms
separately to amplify the amplitudes of a desired pattern
in an unstructured dataset. These quantum search
operators were applied to symmetric and antisymmetric
input superpositions on a three-qubit system for 20
iterations each. After each iteration, different probabilities
of classification were calculated in order to determine the
accuracy of classification for each of the three quantum
search operators. The results indicated that, in the case of
applying the three quantum search operators to
incomplete superposition input states, the partial diffusion
operator outperformed the other operators with a
probability of correct classification that reached 100% in
certain iterations. It also showed that the classification
accuracy of the fixed-phase operator exceeded the
accuracy of the other two operators by 40% in most cases
when the input state was a uniform superposition, and
some of the basis states were phase-inverted.