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International Informatics and Software Engineering Conference (IISEC)

Performance Comparison of Population-Based Quantum-Inspired Evolutionary Algorithms

Hasan Yetış, Mehmet Karaköse

Published Nov 1, 2019• 10 citations
Publisher

Abstract

Quantum computers are seen as the next generation computing technique with the processing power potential they have. However, currently, quantum computers are limited in terms of hardware and algorithmic capabilities. In this study, quantum-inspired methods which are formed by combining quantum computation techniques with classical algorithms are focused on. It has been emphasized in many studies that quantum-inspired methods provide advantages especially for metaheuristic methods. Different from them, in this study, the performance of population-based quantum-inspired methods are compared. The paper focuses on solving the same optimization problem by using quantum-inspired versions of the population-based optimization algorithms such as evolutionary algorithm, genetic algorithm, and differential evolution algorithm. The experimental results show that, while Quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm is better at local search and more accurate results.

DOI
10.1109/ubmyk48245.2019.8965624

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Quantum computers are seen as the next generation computing technique with the processing power potential they have. However, currently, quantum computers are limited in terms of hardware and algorithmic capabilities. In this study, quantum-inspired methods which are formed by combining quantum computation techniques with classical algorithms are focused on. It has been emphasized in many studies that quantum-inspired methods provide advantages especially for metaheuristic methods. Different from them, in this study, the performance of population-based quantum-inspired methods are compared. The paper focuses on solving the same optimization problem by using quantum-inspired versions of the population-based optimization algorithms such as evolutionary algorithm, genetic algorithm, and differential evolution algorithm. The experimental results show that, while Quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm is better at local search and more accurate results.
Quantum-inspired algorithms (QIAs) are computationally efficient algorithms that generate a quantum model in classical hardware as an alternative to the use of quantum models. In this research paper, the researchers carried out an experimental appraisal of four quantum-inspired algorithms in quantum-inspired Evolutionary Algorithm (QEA), quantum-behaved Particle Swarm Optimization (QPSO), quantum-inspired Genetic Algorithm (QIGA) and quantum Simulated Annealing (QSA) on benchmark optimization functions. The introduction of a simulation-based methodology was done in Python to test the convergence speed, global search capability, accuracy and robustness. According to the experimental findings, QPSO always performs well compared with other methods in the achievement of faster convergence and enhanced global optimality, but QEA is highly successful in cases of discrete combinatorial problems. The paper concludes that quantum-inspired models are effective in modeling the key quantum concepts like probabilistic superposition and tunneling to attain high quality optimization results with conventional computing platforms. Such results demonstrate the opportunities of QIAs as scalable pre-quantum tools of designing, AI, and industrial optimization.
The increasing interest, in the last two decades, of evolutionary computation community in the combination of quantum computing and evolutionary computing, has led to the definition of a novel class of quantum-inspired evolutionary algorithms which exploit the concepts of quantum bits and quantum gates with the aim of improving the efficiency of current optimization methods. In this paper, a new method which hybridizes differential evolution with quantum computing is proposed. The method consists of two phases. In the first phase, it performs a new quantum-inspired evolutionary method until it does not get stuck into a local optimum, then, in the second phase, it runs differential evolution by using as initial population that obtained at the end of the first phase. Experiments on classical benchmark functions show that the hybridization outperforms standard methods by sensibly improving the fitness value and speeding up the convergence process.
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.
Quantum computers have made significant progress in the last two decades showing great potential in tackling some of the most challenging problems in computing. This ongoing progress creates an opportunity to implement and evaluate quantum-inspired metaheuristics on real quantum devices, with the aim of uncovering potential computational advantages. Additionally, the practical constraints associated with current quantum computers have highlighted a critical need for classical heuristic methods to optimize the tunable parameters of quantum circuits. Nature-inspired metaheuristics have emerged as promising candidates for fulfilling this optimization role. In this paper, we discuss both of these potential directions at the intersection of evolutionary computing and quantum computing while surveying some of the most promising advancements in these directions. We start with the review of quantum-inspired metaheuristics and then explore implementations of some of these quantum-inspired algorithms on physical quantum devices, capitalizing on the progress in quantum computing technology. Furthermore, we investigate the role of nature-inspired metaheuristics in enhancing the performance of noisy intermediate-scale quantum computers by fine-tuning their parameters. Finally, we discuss some of the recent progress at the intersection of both computing frameworks to highlight the current status and potential of the currently available quantum computing hardware. Synergies between these two computing frameworks demonstrate the potential of a strongly symbiotic relation that can contribute to the simultaneous advancements in both of these computing paradigms.
In this paper, we propose a novel quantum-inspired evolutionary algorithm, called NQEA, for solving combinatorial optimization problems. NQEA uses a new Q-bit update operator to increase the balance between the exploration and exploitation of the search space. In the operator, first, the Q-bits of each individual in the population are updated based on the personal best measurement of that individual and the best measurement of current generation. Then, a restriction is applied to each Q-bit to prevent the premature convergence of its values. The results of experiments on the 0-1 knapsack and NK-landscapes problems show that NQEA performs better than a classical genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms, QEA and vQEA, in terms of convergence speed and accuracy.