Algorithm for Finding the Maximum Clique Based on Continuous Time Quantum Walk

12/05/2019
by   Xi Li, et al.
0

In this work, we consider the application of continuous time quantum walking(CTQW) to the Maximum Clique(MC) Problem. Performing CTQW on graphs will generate distinct periodic probability amplitude for different vertices. We will show that the intensity of the probability amplitude at frequency indeed implies the clique structure of some special kinds of graph. And recursive algorithms with time complexity O(N^5) in classical computers for finding the maximum clique are proposed. We have experimented on random graphs where each edge exists with probabilities 0.3, 0.5 and 0.7. Although counter examples are not found for random graphs, whether these algorithms are universal is not known to us.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/06/2022

Expanded-clique graphs and the domination problem

Given a graph G and a function f : V(G) →ℕ such that f(v_i) ≥ d(v_i) for...
research
08/04/2021

Spatial Search on Johnson Graphs by Continuous-Time Quantum Walk

Spatial search on graphs is one of the most important algorithmic applic...
research
09/27/2021

Application of graph theory in quantum computer science

In this dissertation we demonstrate that the continuous-time quantum wal...
research
03/10/2020

Circuit Synthesis of Maximum Clique Problem using Combinatorial Approach of Classical-Quantum Hybrid Model

In the Maximum Clique Problem, the objective is to find a clique whose s...
research
09/26/2011

Dynamic Local Search for the Maximum Clique Problem

In this paper, we introduce DLS-MC, a new stochastic local search algori...
research
02/25/2018

Cakewalk Sampling

Combinatorial optimization is a common theme in computer science which u...
research
08/04/2023

Group-k consistent measurement set maximization via maximum clique over k-Uniform hypergraphs for robust multi-robot map merging

This paper unifies the theory of consistent-set maximization for robust ...

Please sign up or login with your details

Forgot password? Click here to reset