Phase Matching for a Generalized Grover's Algorithm

TL;DR

This paper generalizes Grover's algorithm by optimizing phase changes at each step to maximize target observation probability, revealing that phase matching is optimal only until near certainty is achieved.

Contribution

It introduces a method to determine optimal phase changes in generalized Grover's algorithm, especially near the final iterations where phase matching no longer applies.

Findings

Optimal phase changes differ from π near probability 1.

Classical Grover's and phase matching are optimal until high target probability.

Numerical and analytical analysis of phase optimization behavior.

Abstract

We study the fully generalized Grover's algorithm to find the optimal phase changes for each step of the iteration to maximize gain in probability of observation of the target, and when phase matching is required. We find that classical Grover's algorithm and phase matching remains to be optimal till the target probability gets close 1. However, as the probability of observation approaches 1, the optimal phase changes differ from $\pi$ and no longer observe phase matching. We provide the optimization statement to find the optimal phase changes given the current amplitude vector and the size of the set. To analyze this formula, we approach it from a numerical and analytical perspective, with the analytical perspective focusing on special cases that simplify the optimization and allow for general statements about its behavior. Finally, we provide an example of a 5 qubit system and show that for the final iteration the optimal phase changes differ from traditional Grover's algorithm and do not observe phase matching, but lead to an increase in the probability of the target.