Quantum-Inspired Evolutionary Algorithms for Optimization Problems

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the application of quantum-inspired techniques to enhance the performance of evolutionary algorithms (EAs) for solving complex optimization problems. Traditional EAs often struggle with slow convergence rates, particularly in high-dimensional search spaces. We propose a novel approach that integrates concepts from quantum mechanics, specifically superposition and interference, into the selection and mutation operators of EAs. The core idea is to leverage the ability of quantum systems to explore multiple possibilities simultaneously, thereby accelerating the convergence process. We demonstrate the effectiveness of this hybrid algorithm through theoretical analysis and illustrate its potential through detailed mathematical formulations. The results indicate a significant improvement in convergence speed and robustness compared to standard EAs, suggesting a promising direction for future research in optimization. The primary focus is on developing an EA that mimics quantum superposition to examine multiple solutions simultaneously and then uses interference to guide the search towards optimal solutions. This approach aims to address the limitations of conventional EAs, particularly in challenging optimization scenarios.

DOI: 10.5281/zenodo.22152619

Related papers

Quantum-Inspired Evolutionary Algorithms for Optimization

Jincheng Zhang

This paper investigates the potential of incorporating quantum mechanical concepts into evolutionary algorithms (EAs) to improve their optimization capabilities. Traditional EAs, while effective, often struggle with complex search spaces and can get trapped in local optima. We propose a framework for developing quantum-inspired EAs that leverages principles such as superposition and entanglement to enhance exploration and exploitation strategies. The core idea is to introduce probabilistic operators and adaptive parameter control mimicking quantum behaviors. We present a detailed description of the proposed algorithm, focusing on the mathematical formulation of the key components, including the representation of solutions, the selection process, and the mutation operator. The algorithm's performance is evaluated on several benchmark optimization problems, demonstrating its potential to outperform conventional EAs in scenarios demanding high accuracy and rapid convergence. Furthermore, we analyze the theoretical implications of this approach, highlighting the advantages derived from quantum-inspired mechanisms. This work contributes to the ongoing research at the intersection of evolutionary computation and quantum information theory, offering a novel approach to tackling complex optimization challenges.

Sep 3, 20260 citations

Quantum-Inspired Evolutionary Algorithms for Complex Optimization

Jincheng Zhang

This paper presents a novel approach to complex optimization problems by integrating quantum-inspired concepts within an evolutionary algorithm framework. Traditional evolutionary algorithms, such as Genetic Algorithms and Differential Evolution, can struggle with high-dimensional and complex search spaces, often exhibiting slow convergence rates. We hypothesize that principles derived from quantum mechanics, specifically superposition and entanglement, can provide a more effective mechanism for exploring these spaces. Our proposed Quantum-Inspired Evolutionary Algorithm (QIEA) incorporates operators designed to mimic quantum interference and entanglement, allowing for a more nuanced and potentially faster exploration of the solution landscape. The core claim is that these quantum-inspired mechanisms can significantly enhance the efficiency of evolutionary optimization. The paper details the algorithm's design, including the implementation of interference and entanglement operators, and discusses its potential advantages and challenges. Experimental results (though not presented through dedicated experimental sections) demonstrate the algorithm's ability to outperform standard evolutionary algorithms on a range of benchmark problems.

Aug 29, 20260 citations

Quantum-Inspired Genetic Algorithms for Complex Optimization Problems

Jincheng Zhang

This paper investigates the application of quantum-inspired concepts within a genetic algorithm framework to address complex optimization challenges. Traditional genetic algorithms often struggle with slow convergence rates when dealing with intricate problem landscapes. We propose a novel approach that incorporates superposition and entanglement, mimicking key principles from quantum mechanics, into the genetic algorithm's operator design. The core idea is to leverage the ability of quantum systems to explore multiple possibilities simultaneously, thereby accelerating the search for optimal solutions. The algorithm's performance is evaluated against established genetic algorithms on a series of benchmark problems. The results demonstrate the potential of this quantum-inspired approach to significantly improve convergence speed and overall efficiency in tackling complex optimization tasks. Specifically, we analyze the impact of these operators on population diversity and the algorithm's ability to escape local optima. The paper concludes with a discussion of future research directions and potential applications of this hybrid algorithm.

Sep 2, 20260 citations

Quantum-Inspired Optimization of Genetic Algorithms for Complex Search Spaces

Jincheng Zhang

This paper explores the integration of quantum-inspired concepts into Genetic Algorithms (GAs) to improve their performance in complex search spaces. Traditional GAs often struggle with the computational cost of evaluating numerous candidate solutions, particularly in high-dimensional spaces. We propose a novel approach where candidate solutions are represented in a superposition of states, mimicking the quantum mechanical principle of superposition. Furthermore, we introduce tunneling mechanisms to enable rapid traversal of the search space, inspired by the quantum tunneling effect. The core idea is to leverage the ability of quantum systems to exist in multiple states simultaneously, allowing the GA to explore a wider range of potential solutions concurrently. The implementation details and the theoretical underpinnings of this hybrid approach are presented, demonstrating the potential for significant acceleration in GA convergence and solution quality. The efficacy of this quantum-inspired GA is demonstrated through an analysis of its performance compared to standard GAs on several benchmark problems, illustrating its ability to overcome limitations inherent in traditional evolutionary algorithms. The key contribution lies in the combined application of superposition and tunneling, offering a new paradigm for optimizing GAs.

Sep 5, 20260 citations

Quantum-Inspired Evolutionary Algorithms for Dynamic System Optimization

Jincheng Zhang

This paper investigates the application of quantum-inspired principles to enhance the performance of evolutionary algorithms (EAs) when dealing with dynamic system optimization problems. Traditional EAs often struggle with rapidly changing fitness landscapes, leading to high computational costs and potential stagnation. We propose a novel evolutionary algorithm that incorporates concepts such as amplitude encoding and interference learning, directly inspired by quantum superposition and interference. The algorithm's core mechanism involves representing solutions using amplitudes and dynamically adjusting these amplitudes based on interference patterns observed during the search process. We hypothesize that this approach will facilitate more efficient exploration of the solution space, particularly in scenarios where the fitness landscape is highly dynamic. Preliminary analysis suggests the potential for significant speedups compared to conventional EAs, representing a shift from solely relying on classical computational techniques to incorporating fundamental quantum phenomena for optimization. The key contribution lies in the adaptation of quantum concepts for evolutionary search, rather than directly implementing quantum computing algorithms.

Aug 28, 20260 citations

Quantum-Enhanced Genetic Algorithms for Complex Optimization

Jincheng Zhang

This paper presents a novel approach to complex optimization problems utilizing a hybrid Genetic Algorithm (GA) enhanced with quantum computing techniques. Classical GAs often struggle with slow convergence rates in high-dimensional search spaces. This research addresses this limitation by integrating quantum superposition and interference into the core operations of the GA, specifically crossover and mutation. The proposed method, termed Quantum-Enhanced Genetic Algorithm (Q-GA), aims to significantly accelerate the optimization process by exploring the solution space more efficiently. The core idea is to represent potential solutions within a quantum state, allowing for parallel exploration facilitated by superposition. We detail the architecture of the Q-GA, outlining the integration of classical GA components with quantum algorithms. The performance of the Q-GA is theoretically analyzed, focusing on the potential speedup achievable through quantum computation. The results presented demonstrate the promise of this hybrid approach for tackling computationally intensive optimization problems where classical GAs fall short.

Sep 6, 20260 citations