Multi-self-loop Lackadaisical Quantum Walk with Partial Phase Inversion

TL;DR

This paper introduces a novel multi-self-loop lackadaisical quantum walk with partial phase inversion, enhancing quantum search success probabilities on hypercube structures by leveraging quantum interference effects.

Contribution

It proposes a new quantum search algorithm with multiple self-loops and partial phase inversion, improving success probabilities and exploring effects on hypercube structures.

Findings

Success probabilities close to 1 for 1-12 marked vertices.

Achieves search complexity of O(√((n+m)·N)).

Introduces new weight values based on ideal weights.

Abstract

The lackadaisical quantum walk, a quantum analog of the lazy random walk, is obtained by adding a weighted self-loop transition to each state. Impacts of the self-loop weight $l$ on the final success probability in finding a solution make it a key parameter for the search process. The number of self-loops can also be critical for search tasks. This article proposes the quantum search algorithm Multi-self-loop Lackadaisical Quantum Walk with Partial Phase Inversion, which can be defined as a lackadaisical quantum walk with multiple self-loops, where the target state phase is partially inverted. In the proposed algorithm, each vertex has $m$ self-loops, with weights $l' = l/m$, where $l$ is a real parameter. The phase inversion is based on Grover's algorithm and acts partially, modifying the phase of a given quantity $s < m$ of self-loops. On a hypercube structure, we analyzed the situation where $1 \leqslant m \leqslant 30$. We also propose two new weight values based on two ideal weights $l$ used in the literature. We investigated the effects of partial phase inversion in the search for $1$ to $12$ marked vertices. As a result, this proposal improved the maximum success probabilities to values close to $1$ in $O (\sqrt{(n+m)\cdot N})$, where $n$ is the hypercube degree. This article contributes with a new perspective on the use of quantum interferences in constructing new quantum search algorithms.