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Computación y Sistemas

Comparison of Performance of Amazon Braket Using a Quantum Genetic Algorithm

Sergio Rosales, Oscar Montiel, Ulises Orozco-Rosas, Juan J. Tapia, Oscar Castillo

Published Sep 30, 2024• 1 citation
Publisher

Abstract

This study evaluates the performance of a Quantum Genetic Algorithm (QGA) executed on quantum devices. The algorithm was implemented in MATLAB and Python and deployed on the Amazon Web Services (AWS) platform. The QGA was utilized to optimize a set of continuous single-variable functions. The implementation employed the Hadamard quantum gate to initialize the population and the Ry rotation gate for mutation and crossover. The findings revealed significant differences in execution time and costs were observed between the two implementations, undescoring the performance of quantum devices available on AWS. The results demonstrate that the QGA can achieve optimal solutions in a few generations, suggesting its potential for efficiently solving complex problems. However, the costs and availability of quantum devices remain restrictive. This work exemplifies the potential of leveraging AWS cloud-based quantum computing platforms for the research and development of quantum algorithms.

DOI
10.13053/cys-28-3-5178

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This study evaluates the performance of a Quantum Genetic Algorithm (QGA) executed on quantum devices. The algorithm was implemented in MATLAB and Python and deployed on the Amazon Web Services (AWS) platform. The QGA was utilized to optimize a set of continuous single-variable functions. The implementation employed the Hadamard quantum gate to initialize the population and the Ry rotation gate for mutation and crossover. The findings revealed significant differences in execution time and costs were observed between the two implementations, undescoring the performance of quantum devices available on AWS. The results demonstrate that the QGA can achieve optimal solutions in a few generations, suggesting its potential for efficiently solving complex problems. However, the costs and availability of quantum devices remain restrictive. This work exemplifies the potential of leveraging AWS cloud-based quantum computing platforms for the research and development of quantum algorithms.
We propose a gate-based Quantum Genetic Algorithm (QGA) for real-valued global optimization. In this model, individuals are represented by quantum circuits whose measurement outcomes are decoded into real-valued vectors through binary discretization. Evolutionary operators act directly on circuit structures, allowing mutation and crossover to explore the space of gate-based encodings. Both fixed-depth and variable-depth variants are introduced, enabling either uniform circuit complexity or adaptive structural evolution. Fitness is evaluated through quantum sampling, using the mean decoded output of measurement outcomes as the argument of the objective function. To isolate the impact of quantum resources, we compare gate sets with and without the Hadamard gate, showing that superposition consistently improves convergence and robustness across benchmark functions such as the Rastrigin function. Furthermore, we demonstrate that introducing pairwise inter-individual entanglement in the population accelerates early convergence, revealing that quantum correlations among individuals provide an additional optimization advantage. Together, these results show that both superposition and entanglement enhance the search dynamics of evolutionary quantum algorithms, establishing gate-based QGAs as a promising framework for quantum-enhanced global optimization.
Quantum algorithm (QA) with updating of quantum gates and catastrophe of population is compared with quantum genetic algorithm (QGA) by including crossover and mutation for quantum bits. Furthermore, a framework of hybrid quantum GA is proposed which combines the quantum based search and classic genetic search, and hybrid QGA with binary encoding (BQGA) and hybrid QGA with real encoding (RQGA) are presented. Numerical simulation on typical problems show that the performances of RQGA are the best among all testing algorithms and it is much robust on parameters and initial conditions.
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.
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.
Quantum computing is an emerging technology that has the potential to change human history. In this thesis, we propose to dive deep into this new technology through theory background and computational experimentation. We explain the theoretical background needed to understand how quantum computing works, then we use this theory background to make a computational implementation on the AWS cloud using Amazon Braket (i.e. AWS service to use quantum processors). As the result of this project, we deliver a document with the theory background required and the Python- code that implements PCA using Amazon Braket. Computational results are consistent with the theory background described in the thesis.
This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.