A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
This manuscript provides a comprehensive review of the Maximum Clique Problem, a
computational problem that involves finding subsets of vertices in a graph that are all …
computational problem that involves finding subsets of vertices in a graph that are all …
Iterative quantum algorithms for maximum independent set: a tale of low-depth quantum algorithms
LT Brady, S Hadfield - arXiv preprint arXiv:2309.13110, 2023 - arxiv.org
Quantum algorithms have been widely studied in the context of combinatorial optimization
problems. While this endeavor can often analytically and practically achieve quadratic …
problems. While this endeavor can often analytically and practically achieve quadratic …
Where do hard problems really exist?
R Marino - arXiv preprint arXiv:2309.16253, 2023 - arxiv.org
This chapter delves into the realm of computational complexity, exploring the world of
challenging combinatorial problems and their ties with statistical physics. Our exploration …
challenging combinatorial problems and their ties with statistical physics. Our exploration …
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning
Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to model
various NP-hard Combinatorial Optimization problems (CO) in the form of binary variables …
various NP-hard Combinatorial Optimization problems (CO) in the form of binary variables …
Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~\cite {schuetz2022combinatorial},\cite …
D Gamarnik - arXiv preprint arXiv:2306.02555, 2023 - arxiv.org
Recently graph neural network (GNN) based algorithms were proposed to solve a variety of
combinatorial optimization problems, including Maximum Cut problem, Maximum …
combinatorial optimization problems, including Maximum Cut problem, Maximum …
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
Y Ichikawa, Y Arai - arXiv preprint arXiv:2409.02135, 2024 - arxiv.org
Learning-based methods have gained attention as general-purpose solvers because they
can automatically learn problem-specific heuristics, reducing the need for manually crafted …
can automatically learn problem-specific heuristics, reducing the need for manually crafted …
Understanding the Usage of QUBO-based Hamiltonian Function in Combinatorial Optimization over Graphs: A Discussion Using Max Cut (MC) Problem
Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to model
various NP-hard combinatorial optimization problems in the form of binary variables. The …
various NP-hard combinatorial optimization problems in the form of binary variables. The …
[PDF][PDF] Tensor Network Simulations of Higher Depth Recursive Quantum Optimization Algorithms
M Passek - mediatum.ub.tum.de
Combinatorial optimization problems (COPs) arise as important issues in various areas such
as finance, telecommunication or industry. Specific examples in industry are supply chain …
as finance, telecommunication or industry. Specific examples in industry are supply chain …