aqora

Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO)

Endre Boros, Peter L. Hammer, Gabriel Tavares

Journal of HeuristicsFeb 20, 2007142 citations

A pointer network based deep learning algorithm for unconstrained binary quadratic programming problem

Shenshen Gu, Tao Hao, Hanmei Yao

NeurocomputingMar 10, 202029 citations

Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way

Ming Chen, Yingwu Chen, Shang Xiang +5 more

neural information processing systemsJan 1, 20241 citation

Learning Quadratic Unconstrained Binary Optimization Models for Quantum Annealing

Florian Richoux, Jean-François Baffier, Philippe Codognet

Jan 1, 20250 citations

Reinforcement Learning based Quantum Approximate Optimization Algorithm for Vehicle Routing Problem.

Md. Aqib Aman, Fardin Azam Sakib

Reinforcement Learning–based Quantum Approximate Optimization Algorithm (QAOA-RL) is used to solve the Vehicle Routing Problem (VRP) by transforming it into a Quadratic Unconstrained Binary Optimization (QUBO) form. The RL agent learns to optimize QAOA parameters for better route selection. Results show that QAOA-RL achieves a lower total cost compared to exact ILP and heuristic Greedy Search methods. Although the training time is higher, the solution quality improves significantly for larger problem sizes. This study highlights QAOA-RL as a scalable and promising hybrid quantum–classical approach for real-world logistics optimization.Reinforcement Learning–based Quantum Approximate Optimization Algorithm (QAOA-RL) is used to solve the Vehicle Routing Problem (VRP) by transforming it into a Quadratic Unconstrained Binary Optimization (QUBO) form. The RL agent learns to optimize QAOA parameters for better route selection. Results show that QAOA-RL achieves a lower total cost compared to exact ILP and heuristic Greedy Search methods. Although the training time is higher, the solution quality improves significantly for larger problem sizes. This study highlights QAOA-RL as a scalable and promising hybrid quantum–classical approach for real-world logistics optimization.

Transportation research procediaJan 1, 20260 citations

Unconstrained binary quadratic optimization

Erich Steiner, Th. M. Liebling, Komei Fukuda +1 more

Jan 1, 20001 citation

Heuristic algorithms based on deep reinforcement learning for quadratic unconstrained binary optimization

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