Optimization has always been viewed as a central component of many electrical engineering techniques, where it involves designing a complex system with various constraints and competing objectives. The method described in this work proposes a hybrid quantum–classical evolutionary optimization algorithm targeting high-frequency electromagnetic problems. A genetic algorithm with a quantum selection operator that applies high selection pressure while preserving selection diversity is introduced. This change means that stagnation can be reduced without compromising the speed of convergence. This was used on both real quantum hardware as well as quantum simulators. The results demonstrate that the performance of the real quantum devices was deteriorated by the noise in these devices and that simulators would be a useful option. We provide a description of the operation of the proposed evolutionary optimization method with mathematical benchmarks and electromagnetic design problems that show that it outperforms conventional evolutionary algorithms in terms of convergence behavior and robustness.
Optimization has always been viewed as a central component of many electrical engineering techniques, where it involves designing a complex system with various constraints and competing objectives. The method described in this work proposes a hybrid quantum–classical evolutionary optimization algorithm targeting high-frequency electromagnetic problems. A genetic algorithm with a quantum selection operator that applies high selection pressure while preserving selection diversity is introduced. This change means that stagnation can be reduced without compromising the speed of convergence. This was used on both real quantum hardware as well as quantum simulators. The results demonstrate that the performance of the real quantum devices was deteriorated by the noise in these devices and that simulators would be a useful option. We provide a description of the operation of the proposed evolutionary optimization method with mathematical benchmarks and electromagnetic design problems that show that it outperforms conventional evolutionary algorithms in terms of convergence behavior and robustness.
The emergence of quantum computing has brought with it multiple applications like hybrid classic-quantum evolutionary optimization algorithms. In electromagnetics, optimization has always been present to increase the performance of complex systems. This paper presents two new oracles for the genetic algorithm with quantum selection and its newer version that uses simulated binary crossover. These algorithms are applied to two common electromagnetic optimization problems showing strong results and demonstrating the flexibility that these hybrid algorithms have by making small changes to their quantum circuits.
Multi‐objective optimization in quantum systems poses formidable challenges stemming from the high dimensionality, complex constraints, and conflicting performance metrics inherent to quantum control tasks and circuit synthesis. This study presents a comprehensive exploration of genetic AI algorithms—evolutionary strategies inspired by natural selection—tailored specifically for multi‐objective optimization in quantum computing contexts. We propose a hybrid framework that synergistically combines the elitist, Pareto‐based selection mechanics of NSGA-II with quantum‐inspired crossover and mutation operators designed to preserve entanglement structures and exploit hardware‐specific gate sets. In this framework, candidate solutions encoding circuit topologies or control‐pulse parameters undergo iterative evolution, guided by fitness vectors that balance gate fidelity, circuit depth, and resource overhead. To benchmark performance, we apply the algorithm to canonical quantum computing problems: Fourier transform circuits, QAOA ansätze for MaxCut instances, and random Clifford+T circuits. Through extensive simulation on noise-modelled quantum devices, we evaluate Pareto front coverage, convergence speed, diversity maintenance, and robustness under decoherence. Our results demonstrate that the proposed genetic AI algorithm outperforms classical single‐objective genetic methods and established multi‐objective evolutionary algorithms (e.g., SPEA2) by achieving broader Pareto fronts and faster convergence rates, while maintaining solution diversity. Furthermore, noise-resilience experiments show graceful degradation, indicating practical applicability on near-term quantum hardware. These findings substantiate the potential of evolutionary computation paradigms—augmented with quantum‐inspired operators—to address the intricate trade-off surfaces that define NISQ-era optimization problems, paving the way toward automated quantum circuit design and control parameter tuning for real-world quantum computing applications.
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.
Quantum computing is a promising and emerging field of computer science with the potential to improve artificial intelligence techniques, particularly those related to optimization. However, current quantum devices operate in the Noisy Intermediate-Scale Quantum era, in which quantum noise can significantly compromise computational reliability. Evolutionary algorithms are inherently stochastic and may therefore be less sensitive to such noise, or even benefit from it. This study evaluates the performance of a hybrid quantum genetic algorithm on a set of combinatorial and continuous optimization problems under various realistic noise models. The findings demonstrate that quantum noise does not hinder the algorithm's performance and can even enhance its exploratory capabilities in some cases. This suggests that noise-aware quantum evolutionary strategies have the potential to be effective even with today's imperfect quantum hardware.
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.
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.