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

arXiv:2311.03128 · Advances in Artificial Intelligence and Machine Learning

Benchmarking Differential Evolution on a Quantum Simulator

Parthasarathy Srinivasan

Published Jan 1, 2023• 1 citation
arXivHTMLPDF

Abstract

The use of Evolutionary Algorithms (EA) for solving Mathematical/Computational Optimization Problems is inspired by the biological processes of Evolution. Few of the primitives involved in the Evolutionary process/paradigm are selection of ’Fit’ individuals (from a population sample) for retention, cloning, mutation, discarding, breeding, crossover etc. In the Evolutionary Algorithm abstraction, the individuals are deemed to be solution candidates to an Optimization problem and additional solution(/sets) are built by applying analogies to the above primitives (cloning, mutation etc.) by means of evaluating a ’Fitness’ function/criterion. One such algorithm is Differential Evolution(DE) which can be used to compute the minima of functions such as the rastrigin function and rosenbrock function. This work is an attempt to study the result of applying the DE method on these functions with candidate individuals generated on classical Turing modeled computation and comparing the same with those on state of the art Quantum computation.The study benchmarks the convergence of these functions by varying the parameters initialized and reports timing, convergence, and resource utilization results.

DOI
10.54364/aaiml.2023.1196

Related papers

Hybrid Quantum Differential Evolution

Clara Pizzuti

The increasing interest, in the last two decades, of evolutionary computation community in the combination of quantum computing and evolutionary computing, has led to the definition of a novel class of quantum-inspired evolutionary algorithms which exploit the concepts of quantum bits and quantum gates with the aim of improving the efficiency of current optimization methods. In this paper, a new method which hybridizes differential evolution with quantum computing is proposed. The method consists of two phases. In the first phase, it performs a new quantum-inspired evolutionary method until it does not get stuck into a local optimum, then, in the second phase, it runs differential evolution by using as initial population that obtained at the end of the first phase. Experiments on classical benchmark functions show that the hybridization outperforms standard methods by sensibly improving the fitness value and speeding up the convergence process.

International Conference on Information, Intelligence, Systems & Applications (IISA)•Jul 12, 2021•2 citations

Performance Comparison of Population-Based Quantum-Inspired Evolutionary Algorithms

Hasan Yetış, Mehmet Karaköse

Quantum computers are seen as the next generation computing technique with the processing power potential they have. However, currently, quantum computers are limited in terms of hardware and algorithmic capabilities. In this study, quantum-inspired methods which are formed by combining quantum computation techniques with classical algorithms are focused on. It has been emphasized in many studies that quantum-inspired methods provide advantages especially for metaheuristic methods. Different from them, in this study, the performance of population-based quantum-inspired methods are compared. The paper focuses on solving the same optimization problem by using quantum-inspired versions of the population-based optimization algorithms such as evolutionary algorithm, genetic algorithm, and differential evolution algorithm. The experimental results show that, while Quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm is better at local search and more accurate results.

International Informatics and Software Engineering Conference (IISEC)•Nov 1, 2019•10 citations

A quantum differential evolution algorithm for function optimization

Qiuyan Xu, Jun Guo

In this paper, we propose a quantum differential evolution (QDE) algorithm for function optimization, which can improve the performance of differential evolution (DE) algorithm. First, the algorithm does some basic operations, such as mutation, crossover and selection of vectors. And then a quantum computing method is utilized to search the global optimal solutions, which avoids falling into the local minimums. Our experimental results show the proposed algorithm is feasible. And compared with other algorithms, the proposed algorithm is more effective. Furthermore it can improve the speed of convergence.

International Conference on Computer Application and System Modeling•Oct 1, 2010•3 citations

Quantum Entanglement inspired Differential Evolution algorithm

Abhishek Dixit, Ashish Mani

The Differential Evolution (DE) algorithm is an efficient algorithm with strong search capability. This algorithm is easy to implement and used for solving large-scale optimization problems. However, DE also has drawbacks of insufficient diversity in the later search stage, while solving optimization problems, slow convergence speed, and a high search stagnation possibility. Quantum Entanglement inspired Differential Evolution algorithm (QE-DE) has been proposed in this paper to solve the drawbacks of DE. For solving high-dependency optimization problems, QE-DE integrates entangled states in its Qubits (or quantum bit). The optimization process is further accelerated by utilizing the quantum local search. The proposed QE-DE algorithm is evaluated on the IEEE Congress of Evolutionary Computing (CEC 2017) benchmark set for 10 and 30-dimensions. QE-DE is also compared with existing variants of DE and some other popular algorithms. QE-DE outperforms the performance comparison results.

Genetic and Evolutionary Computation Conference Companion (GECCO Companion)•Jul 15, 2023•9 citations

Quantum-enhanced selection operators for evolutionary algorithms

David Von Dollen, Sheir Yarkoni, Daniel Weimer +2 more

Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. Our approach frames the selection process as a minimization of a binary quadratic model with which we encode fitness and distance between members of a population, and we leverage a quantum annealing system to sample low energy solutions for the selection mechanism. We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization. We observe a performance gain in average number of generations to convergence for the quantum-enhanced elitist selection operator in comparison to classical on the OneMax function. We also find that the quantum-enhanced selection operator with non-elitist selection outperform benchmarks on functions with fitness perturbation from the IOHProfiler library. Additionally, we find that in the case of elitist selection, the quantum-enhanced operators outperform classical benchmarks on functions with varying degrees of dummy variables and neutrality.

arXiv:2206.10743•Genetic and Evolutionary Computation Conference Companion (GECCO Companion)•Jul 9, 2022•5 citations

Title: Quantum-Enhanced Differential Evolution – Optimization for Evolutionary Algorithms

Jincheng Zhang

Quantum-enhanced differential evolution (QED) offers a promising approach to accelerate the optimization of complex evolutionary algorithms, particularly in scenarios with high-dimensional search spaces. This paper proposes a novel method that integrates quantum annealing with the differential evolution framework, leveraging quantum computing to reduce computational cost. We investigate the potential of this hybrid approach to improve the performance of evolutionary algorithms in diverse fields, demonstrating significant speedups compared to classical counterparts. The core mechanism centers on strategically employing quantum annealing to perform computationally intensive sub-problems within the evolutionary process. This allows for a more efficient exploration of the search space, ultimately leading to faster convergence and improved solution quality. We explore various quantum annealing techniques and parameter tuning strategies to optimize the QED algorithm for specific optimization problems. The results showcase the efficacy of this hybrid approach, highlighting its potential to revolutionize the optimization of complex, high-dimensional problems.

•Sep 7, 2026•0 citations
The use of Evolutionary Algorithms (EA) for solving Mathematical/Computational Optimization Problems is inspired by the biological processes of Evolution. Few of the primitives involved in the Evolutionary process/paradigm are selection of ’Fit’ individuals (from a population sample) for retention, cloning, mutation, discarding, breeding, crossover etc. In the Evolutionary Algorithm abstraction, the individuals are deemed to be solution candidates to an Optimization problem and additional solution(/sets) are built by applying analogies to the above primitives (cloning, mutation etc.) by means of evaluating a ’Fitness’ function/criterion. One such algorithm is Differential Evolution(DE) which can be used to compute the minima of functions such as the rastrigin function and rosenbrock function. This work is an attempt to study the result of applying the DE method on these functions with candidate individuals generated on classical Turing modeled computation and comparing the same with those on state of the art Quantum computation.The study benchmarks the convergence of these functions by varying the parameters initialized and reports timing, convergence, and resource utilization results.
The increasing interest, in the last two decades, of evolutionary computation community in the combination of quantum computing and evolutionary computing, has led to the definition of a novel class of quantum-inspired evolutionary algorithms which exploit the concepts of quantum bits and quantum gates with the aim of improving the efficiency of current optimization methods. In this paper, a new method which hybridizes differential evolution with quantum computing is proposed. The method consists of two phases. In the first phase, it performs a new quantum-inspired evolutionary method until it does not get stuck into a local optimum, then, in the second phase, it runs differential evolution by using as initial population that obtained at the end of the first phase. Experiments on classical benchmark functions show that the hybridization outperforms standard methods by sensibly improving the fitness value and speeding up the convergence process.
Quantum computers are seen as the next generation computing technique with the processing power potential they have. However, currently, quantum computers are limited in terms of hardware and algorithmic capabilities. In this study, quantum-inspired methods which are formed by combining quantum computation techniques with classical algorithms are focused on. It has been emphasized in many studies that quantum-inspired methods provide advantages especially for metaheuristic methods. Different from them, in this study, the performance of population-based quantum-inspired methods are compared. The paper focuses on solving the same optimization problem by using quantum-inspired versions of the population-based optimization algorithms such as evolutionary algorithm, genetic algorithm, and differential evolution algorithm. The experimental results show that, while Quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm is better at local search and more accurate results.
In this paper, we propose a quantum differential evolution (QDE) algorithm for function optimization, which can improve the performance of differential evolution (DE) algorithm. First, the algorithm does some basic operations, such as mutation, crossover and selection of vectors. And then a quantum computing method is utilized to search the global optimal solutions, which avoids falling into the local minimums. Our experimental results show the proposed algorithm is feasible. And compared with other algorithms, the proposed algorithm is more effective. Furthermore it can improve the speed of convergence.
The Differential Evolution (DE) algorithm is an efficient algorithm with strong search capability. This algorithm is easy to implement and used for solving large-scale optimization problems. However, DE also has drawbacks of insufficient diversity in the later search stage, while solving optimization problems, slow convergence speed, and a high search stagnation possibility. Quantum Entanglement inspired Differential Evolution algorithm (QE-DE) has been proposed in this paper to solve the drawbacks of DE. For solving high-dependency optimization problems, QE-DE integrates entangled states in its Qubits (or quantum bit). The optimization process is further accelerated by utilizing the quantum local search. The proposed QE-DE algorithm is evaluated on the IEEE Congress of Evolutionary Computing (CEC 2017) benchmark set for 10 and 30-dimensions. QE-DE is also compared with existing variants of DE and some other popular algorithms. QE-DE outperforms the performance comparison results.
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. Our approach frames the selection process as a minimization of a binary quadratic model with which we encode fitness and distance between members of a population, and we leverage a quantum annealing system to sample low energy solutions for the selection mechanism. We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization. We observe a performance gain in average number of generations to convergence for the quantum-enhanced elitist selection operator in comparison to classical on the OneMax function. We also find that the quantum-enhanced selection operator with non-elitist selection outperform benchmarks on functions with fitness perturbation from the IOHProfiler library. Additionally, we find that in the case of elitist selection, the quantum-enhanced operators outperform classical benchmarks on functions with varying degrees of dummy variables and neutrality.
Quantum-enhanced differential evolution (QED) offers a promising approach to accelerate the optimization of complex evolutionary algorithms, particularly in scenarios with high-dimensional search spaces. This paper proposes a novel method that integrates quantum annealing with the differential evolution framework, leveraging quantum computing to reduce computational cost. We investigate the potential of this hybrid approach to improve the performance of evolutionary algorithms in diverse fields, demonstrating significant speedups compared to classical counterparts. The core mechanism centers on strategically employing quantum annealing to perform computationally intensive sub-problems within the evolutionary process. This allows for a more efficient exploration of the search space, ultimately leading to faster convergence and improved solution quality. We explore various quantum annealing techniques and parameter tuning strategies to optimize the QED algorithm for specific optimization problems. The results showcase the efficacy of this hybrid approach, highlighting its potential to revolutionize the optimization of complex, high-dimensional problems.