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European Conference on Antennas and Propagation

Novel Quantum Computation Based Selection Operator for Genetic Algorithms Applied to Electromagnetic Problems

Gabriel F. Martinez E., Riccardo Enrico Zich

Published Mar 17, 2024• 7 citations
Publisher

Abstract

Quantum computing is an extremely promising tool that could dramatically change many engineering fields where computationally heavy tasks have to be dealt with such as electromagnetic optimization. Electromagnetic problems, in fact, are extremely computationally expensive, and an heuristic optimization may need thousands of generations. In this paper, the problems and the potential of the introduction of quantum computing in optimization has been investigated. Numerical results on the preliminary test are provided here while more detailed results will be directly presented at the conference.

DOI
10.23919/eucap60739.2024.10501371

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Quantum computing is an extremely promising tool that could dramatically change many engineering fields where computationally heavy tasks have to be dealt with such as electromagnetic optimization. Electromagnetic problems, in fact, are extremely computationally expensive, and an heuristic optimization may need thousands of generations. In this paper, the problems and the potential of the introduction of quantum computing in optimization has been investigated. Numerical results on the preliminary test are provided here while more detailed results will be directly presented at the conference.
The introduction of quantum computing is right now dramatically impacting the research activities in many engineering fields, in particular where a lot of data have to be considered, stored, analyzed to find the best possible strategy to tackle a complex problem, such as dealing with electromagnetic (EM) optimization.In fact, EM problems are critical from a computational point of view, because they often require thousands of different numerical solutions that can be obtained by extremely time and resource consuming numerical solvers both in the frequency or in the time domain.In this framework, the combined application of quantum computing and evolutionary optimization algorithms can have a great effectiveness to manage the electromagnetic optimization of complex structures.
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.
By violating the extended Church-Turing thesis, the quantum computing (QC) paradigm has emerged as a serious contender to classical computing. We now know that certain classical exponential problems (NP (nondeterministic polynomial time)-complete) can be solved in polynomial time by QC. Shor’s integer factorization and Grover’s method for unstructured search are perhaps the most well-known quantum algorithms. QC has been successfully applied to a variety of domains including optimization problems. Optimization can be solved by using deterministic/numerical as well as stochastic methods. Genetic algorithms (GA) belong to the latter category. Incorporating quantum mechanical properties such as superposition, entanglement, interference into the structure of GA has resulted in a more efficient evolutionary computing method called the quantum-inspired genetic algorithm. This method has been successfully applied to image processing, signal processing, economic load dispatch, coalition formation, task scheduling, etc. This chapter presents an overview of the current trends and developments in the domain of quantum-inspired genetic algorithms with the aim of investigating areas of further research in this sphere.
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.
The ability of theoretical and monadic quantum models against number comparison to conventional Quantum Genetic Algorithm (QGA), quantitative particle swarm optimization, ant colony groups with simulated annealing types is analyzed in the context of electrical engineering. Several experiments were done, involving a collection of multiple data sets standard for the proposed circuit layout within power distribution and signal processing applications. In regard to comparative analysis, each algorithm presents its particular strengths for QGA-competitive convergence speed; ͟QPSO – quick conversion of individuals into a global optimum during evolution’s course and robust solutions quality in an unstable environment without tuning. The related works of the proposed algorithms include evaluating it against metaheuristics for power systems, nature-inspired hybrid heuristic approaches as well as physics motivated optimization schemes. The paper emphasizes the superiority of quantum algorithms over their classical counterparts, which is a major innovation in optimization space. This detailed analysis contributes to further comprehending the potentials of quantum computing in overcoming tough optimization challenges faced by electrical engineers.
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.