Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Implementable Hybrid Quantum Ant Colony Optimization Algorithm

Garcia de Andoin Mikel, Javier Echanobe

Published Jul 8, 2021• 0 citations
OpenAlex

Abstract

We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to solve NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codes only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths using Fredkin gates without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.

Related papers

Implementable hybrid quantum ant colony optimization algorithm

Mikel Garcia de Andoin, Javier Echanobe

Abstract We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to produce approximate solutions for NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codifies only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.

arXiv:2107.03845•Quantum Machine Intelligence•Jun 17, 2022•10 citations

Hybrid Quantum Ant Colony Algorithm for Traveling Salesman Problem

Jia Rui-y

Aiming at the Traveling Salesman Problem based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate,a hybrid quantum ant colony optimization algorithm is presented.In this algorithm,the pheromone on each path is encoded by a group of quantum bits,the quantum rotation gate and ant's tour are used to update the pheromone so as to accelerate its convergence speed.To avoid the search falling into local optimum,the new neighborhood exchange strategy is designed to improve solution efficiency.Some cases from the TSP library(TSPLIB)are used to experiment,the results show that the algorithm has rapider convergence speed and higher accuracy than the classical ant colony algorithm.

Computer Engineering and Applications Journal•Jan 1, 2013•0 citations

Algoritmos cuánticos y Quantum Ant Colony Optimization

Mikel Garcia de Andoin

[EN] I propose an improved algorithm that can be implemented on a real quantum computer with a new guided exploration strategy. The benchmarks made by simulating the quantum circuit shows that the time to solve a problem using QACO could outperform greatly other classical bio-inspired algorithms, in particular the one QACO is based on, the Ant Colony Optimization. Quantum computation is on the verge of becoming the new technology for solving time- and energy-consuming problems. To make use of the full potential of quantum computers one must implement algorithms that are significantly different from the ones used on digital computers. This work serves as an introduction to both quantum computing and algorithms. Starting from the basis of the quantum physics, I expose each of the steps needed to understand the mathematics beneath this topic. Using this basis, I develope one quantum algorithm from scratch. Based on a previously proposed Quantum Ant Colony Optimization (QACO) algorithm, I propose an improved algorithm that can be implemented on a real quantum computer with a new guided exploration strategy. The benchmarks made by simulating the quantum circuit shows that the time to solve a problem using QACO could outperform greatly other classical bio-inspired algorithms, in particular the one QACO is based on, the Ant Colony Optimization.

•Dec 15, 2020•0 citations

Hybrid Quantum Approximate Optimization Using Enhanced Ant Colony Optimization to Solve Large-Scale Combinatorial Optimization Problems

Bishad Ghimire, Ausif Mahmood, Khaled M. Elleithy

Quantum computing is a promising technology that may provide breakthrough solutions to today’s difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. A class of algorithms referred to as Quantum Approximate Optimization Algorithm (QAOA) have been recently proposed. QAOA attempts to approximately solve hard problems using a protocol know as bang-bang. The technique is based on the unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. QAOA has been explored in the context of several optimization problems such as Max-Cut problem, variational Eigenvalue problem etc. Recently, attempts have been made to create QAOA for the Traveling Salesman Problem (TSP). Due to small node size and limited solution capability of the currently available Quantum computers and/or simulators, we develop a hybrid approach where subgraphs of a TSP tour are executed on a Quantum computer and the results from the quantum optimization are combined in further optimization of the whole tour. Since the local optimization of a subgraph is prone to getting stuck in a local minima, we overcome this problem by using a parallel Ant Colony Optimization Algorithm with periodic pheromone exchange between colonies. Our results are encouraging and yield optimum results for benchmarks with less than 50 nodes, and usually within 1% of the optimal solution for benchmarks with around 200 nodes.

International Conference on Soft Computing & Machine Intelligence (ISCMI)•Nov 26, 2021•6 citations

Quantum-inspired ant algorithm for knapsack problems

Wang Hong-gang, Ma Liang, Huizhen Zhang +1 more

The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard. This paper proposes a new algorithm called quantum-inspired ant algorithm (QAA) to solve the knapsack problem. QAA takes the advantage of the principles in quantum computing, such as qubit, quantum gate, and quantum superposition of states, to get more probabilistic-based status with small colonies. By updating the pheromone in the ant algorithm and rotating the quantum gate, the algorithm can finally reach the optimal solution. The detailed steps to use QAA are presented, and by solving series of test cases of classical knapsack problems, the effectiveness and generality of the new algorithm are validated.

Journal of Systems Engineering and Electronics•Jan 18, 2012•6 citations

Quantum-inspired ant algorithm for solving 0-1 knapsack problem

Xiaofeng He, Ma Liang

0-1 knapsack problem is a typical NP-hard problem in combinatorial optimization.A quantum-inspired ant colony algorithm for solving the 0-1 knapsack problem is proposed which is based on the combination of ant colony optimization and quantum computing.In the algorithm,the pheromone is expressed by quantum bits,and quantum rotation gates are used to update the ant pheromone.Series of test instances validate the effectiveness of the algorithm.The proposed algorithm can reduce the searching time and has better performance in reaching the global optimum.

Computer Engineering and Applications Journal•Jan 1, 2011•6 citations
We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to solve NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codes only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths using Fredkin gates without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.
Abstract We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to produce approximate solutions for NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codifies only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.
Aiming at the Traveling Salesman Problem based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate,a hybrid quantum ant colony optimization algorithm is presented.In this algorithm,the pheromone on each path is encoded by a group of quantum bits,the quantum rotation gate and ant's tour are used to update the pheromone so as to accelerate its convergence speed.To avoid the search falling into local optimum,the new neighborhood exchange strategy is designed to improve solution efficiency.Some cases from the TSP library(TSPLIB)are used to experiment,the results show that the algorithm has rapider convergence speed and higher accuracy than the classical ant colony algorithm.
[EN] I propose an improved algorithm that can be implemented on a real quantum computer with a new guided exploration strategy. The benchmarks made by simulating the quantum circuit shows that the time to solve a problem using QACO could outperform greatly other classical bio-inspired algorithms, in particular the one QACO is based on, the Ant Colony Optimization. Quantum computation is on the verge of becoming the new technology for solving time- and energy-consuming problems. To make use of the full potential of quantum computers one must implement algorithms that are significantly different from the ones used on digital computers. This work serves as an introduction to both quantum computing and algorithms. Starting from the basis of the quantum physics, I expose each of the steps needed to understand the mathematics beneath this topic. Using this basis, I develope one quantum algorithm from scratch. Based on a previously proposed Quantum Ant Colony Optimization (QACO) algorithm, I propose an improved algorithm that can be implemented on a real quantum computer with a new guided exploration strategy. The benchmarks made by simulating the quantum circuit shows that the time to solve a problem using QACO could outperform greatly other classical bio-inspired algorithms, in particular the one QACO is based on, the Ant Colony Optimization.
Quantum computing is a promising technology that may provide breakthrough solutions to today’s difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. A class of algorithms referred to as Quantum Approximate Optimization Algorithm (QAOA) have been recently proposed. QAOA attempts to approximately solve hard problems using a protocol know as bang-bang. The technique is based on the unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. QAOA has been explored in the context of several optimization problems such as Max-Cut problem, variational Eigenvalue problem etc. Recently, attempts have been made to create QAOA for the Traveling Salesman Problem (TSP). Due to small node size and limited solution capability of the currently available Quantum computers and/or simulators, we develop a hybrid approach where subgraphs of a TSP tour are executed on a Quantum computer and the results from the quantum optimization are combined in further optimization of the whole tour. Since the local optimization of a subgraph is prone to getting stuck in a local minima, we overcome this problem by using a parallel Ant Colony Optimization Algorithm with periodic pheromone exchange between colonies. Our results are encouraging and yield optimum results for benchmarks with less than 50 nodes, and usually within 1% of the optimal solution for benchmarks with around 200 nodes.
The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard. This paper proposes a new algorithm called quantum-inspired ant algorithm (QAA) to solve the knapsack problem. QAA takes the advantage of the principles in quantum computing, such as qubit, quantum gate, and quantum superposition of states, to get more probabilistic-based status with small colonies. By updating the pheromone in the ant algorithm and rotating the quantum gate, the algorithm can finally reach the optimal solution. The detailed steps to use QAA are presented, and by solving series of test cases of classical knapsack problems, the effectiveness and generality of the new algorithm are validated.
0-1 knapsack problem is a typical NP-hard problem in combinatorial optimization.A quantum-inspired ant colony algorithm for solving the 0-1 knapsack problem is proposed which is based on the combination of ant colony optimization and quantum computing.In the algorithm,the pheromone is expressed by quantum bits,and quantum rotation gates are used to update the ant pheromone.Series of test instances validate the effectiveness of the algorithm.The proposed algorithm can reduce the searching time and has better performance in reaching the global optimum.