Circuit Implementation and Analysis of a Quantum-Walk Based Search Complement Algorithm

TL;DR

This paper introduces a modified quantum walk search algorithm called the search complement, which reduces the target state's measurement probability by altering the evolution operator, and demonstrates its implementation on IBM quantum hardware.

Contribution

It presents a novel variation of the quantum walk search algorithm that decreases target state probability and provides a detailed multigraph analysis of this behavior.

Findings

The modified algorithm reduces target state probability due to fewer paths leading to it.

Experimental implementation on IBM quantum processor achieved low statistical distances.

Theoretical analysis explains the decreased probability via multigraph and matrix approaches.

Abstract

We propose a modified version of the quantum walk-based search algorithm created by Shenvi, Kempe and Whaley, also known as the SKW algorithm. In our version of the algorithm, we modified the evolution operator of the system so that it is composed by the product of the shift operator associated to the $2^n$-complete graph with self-loops and a perturbed coin operator based on the Hadamard operator that works as an oracle for the search. The modified evolution operator leads the opposite behavior as in the original algorithm, that is, the probability to measure the target state is reduced. We call this new behavior the $\textit{search complement}$. Taking a multigraph and matrix approach, we were able to explain that the new algorithm decreases the probability of the target state given that there are less paths that lead towards the node that is associated to the target state in a Unitary Coined Discrete-Time Quantum Walk. The search complement algorithm was executed experimentally on IBM quantum processor $\textit{ibmq_manila}$ obtaining statistical distances $\ell_1\leq 0.0895$ when decreasing the probability of one state out of four.