Quantum Searching via Entanglement and Partial Diffusion

In this paper, we will define a quantum operator that performs the inversion about the mean only on a subspace of the system (Partial Diffusion Operator). This operator is used in a quantum search algorithm that runs in O(sqrt{N/M}) for searching an unstructured list of size N with M matches such that 1<= M<=N. We will show that the performance of the algorithm is more reliable than known {fixed operators quantum search algorithms} especially for multiple matches where we can get a solution after a single iteration with probability over 90% if the number of matches is approximately more than one-third of the search space. We will show that the algorithm will be able to handle the case where the number of matches M is unknown in advance such that 1<=M<=N in O(sqrt{N/M}). A performance comparison with Grover's algorithm will be provided.