Quantum Genetic Algorithms (QGAs) are emerging as a new class of algorithms that exploit the peculiarities of quantum computers to outperform classical evolutionary approaches in solving complex optimization problems. Despite the theoretical promises, their applicability has been strongly affected by the limitation of current noise-prone quantum hardware. It has only recently been demonstrated that acceptable levels of quantum noise can enhance the exploration capability of QGA in constrained optimization problems. The research corroborates the initial findings, providing further evidence of the efficacy of these algorithms by successfully resolving the NP-hard Ising Problem through the utilization of a QGA equipped with a quantum mating operator. The experimental results show that noisy simulations of QGA are capable of identifying better solutions than genetic algorithms that are equipped with state-of-the-art classical mating operators.
Quantum Genetic Algorithms (QGAs) are emerging as a new class of algorithms that exploit the peculiarities of quantum computers to outperform classical evolutionary approaches in solving complex optimization problems. Despite the theoretical promises, their applicability has been strongly affected by the limitation of current noise-prone quantum hardware. It has only recently been demonstrated that acceptable levels of quantum noise can enhance the exploration capability of QGA in constrained optimization problems. The research corroborates the initial findings, providing further evidence of the efficacy of these algorithms by successfully resolving the NP-hard Ising Problem through the utilization of a QGA equipped with a quantum mating operator. The experimental results show that noisy simulations of QGA are capable of identifying better solutions than genetic algorithms that are equipped with state-of-the-art classical mating operators.
In recent years, quantum computing is finding strong application in the development of new evolutionary algorithms. This is mainly due to the intrinsic parallelism induced by this new computational paradigm, which fits well with the structure of population-based optimization algorithms. However, the pioneering nature of quantum hardware makes quantum computations still suffer from noise that affects their accuracy and precision. This study aims to analyze the effect of this noise on quantum genetic algorithms and to quantify how much this can destroy or enhance the guided search of these algorithms in the search space. In detail, the proposed paper introduces a systematic study that assess the effects of quantum noise in a genetic algorithm equipped with a quantum recombination operator, formally known as the Quantum Mating Operator, when applied to solving a well-known optimization problem such as the 01 knapsack.
Genetic algorithms are widely used for solving optimization problems. As the search space of the problem is augmented, the probability of falling into local minima will be augmented as well. Previous works showed that the adjusted phase (i.e. required modification per generation), may enhance badly the optimality reaching. Moreover, the search for suitable probabilities on crossover and mutation results in huge computational complexity. Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers. This paper will be oriented toward computer scientists interested in the quantum genetic algorithm (QGA). Therefore, we will present a discussion, of the pros, and cons of classical GA. We survey the “state of the art” of the most known techniques for evolving evolutionary algorithms and quantum computing. We intend in this work to help researchers and application developers in gaining insights into the power of quantum computing mechanisms. This science can inherently treat the balance between exploration and exploitation of the search space. This property is due to the use of Q-bits. The orientation toward the best solution is due to the use of Q-gates.
Genetic Algorithms (GAs) are optimization methods that search near-optimal solutions by applying well-known operations such as selection, crossover and mutation. In particular, crossover and mutation are aimed at creating new solutions from selected parents with the goal of discovering better and better solutions in the search space. In literature, several approaches have been defined to create new solutions from the mating pool to try to improve the performance of genetic optimization. In this paper, the literature is enriched by introducing a new mating operator that harnesses the stochastic nature of quantum computation to evolve individuals in a classical genetic workflow. This new approach, named Quantum Mating Operator, acts as a multi-parent operator that identifies alleles' frequency patterns from a collection of individuals selected by means of conventional selection operators, and encodes them through a quantum state. This state is successively mutated and measured to generate a new classical chromosome. As shown by experimental results, GAs equipped with the proposed operator outperform those equipped with traditional crossover and mutation operators when used to solve well-known benchmark functions.
Quantum Genetic Algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy (i.e., quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum Genetic Algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover's algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome (i.e., the solution or one of the solutions of the problem) in exponential runtime, [[EQUATION]], where [[EQUATION]] is the number of qubits in the individuals' quantum register. This paper introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the Reduced Quantum Genetic Algorithm (RQGA) complexity problem. Accordingly, we control the complexity of theReduced Quantum Genetic Algorithm (RQGA) algorithm by selecting a limited number of qubits in the individuals' register and fixing the remaining ones as classical values of '0' and '1' with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum Algorithm with Genetic Optimization (HQAGO) solves search problems in [[EQUATION]] oracle queries, where [[EQUATION]] is the number of fixed classical bits in the individuals' register.
This paper presents a novel approach to optimization design leveraging the principles of quantum mechanics, specifically superposition and entanglement, through a Quantum Genetic Algorithm (QGA). Traditional Genetic Algorithms (GAs) face challenges in complex, high-dimensional optimization problems due to the exponential growth of the search space. The QGA aims to mitigate these limitations by encoding genetic information into quantum states and utilizing quantum operations for selection, crossover, and mutation. This approach theoretically utilizes quantum superposition to explore multiple solution possibilities simultaneously and quantum entanglement to correlate solutions, potentially leading to faster convergence and improved optimization performance. The paper outlines the theoretical framework, the algorithmic steps of the QGA, and discusses potential applications. The primary contribution is the conceptual framework and the proposed method, recognizing the nascent stage of QGA research and presenting a novel exploration of its potential.
Quantum genetic algorithms(QGA) typically construct traditional quantum chromosomes as independent quantum bit systems, which has facilitated the enhancement of search speed and precision in genetic algorithms, demonstrating powerful problem-solving capabilities in optimization issues. Although traditional QGA have enriched population diversity, they still tend to fall into local optima, and the evolutionary update operations require rotation directions to be determined by lookup tables, a process involving multiple conditional judgments that affect algorithm efficiency. To address these issues, an improved method has been proposed to further enhance the performance of QGA. In the initial population selection, a population grouping evolution method based on microhabitat coevolution strategy is used to prevent the algorithm from getting trapped in local optima when solving complex function problems. Additionally, quantum entanglement is introduced on top of quantum super-position by implementing quantum entanglement using quantum registers, and higher-order quantum rotation gates are employed that do not involve multiple conditional judgments of lookup tables, thus improving the efficiency of quantum chromosome measurement and evolutionary updates. Finally, traditional QGA and higher-order quantum genetic algorithms were applied to test functions in multiple experiments. The results indicate that, compared to traditional QGA, the higher-order quantum genetic algorithms have achieved an increase in convergence speed, an approximate 14.3% improvement in convergence precision, and possess enhanced global search capabilities.