Quantum Pattern Classification in a Three-Qubit System

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