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Quantum genetic algorithm and its application in non-restriction optimization

Shitong Wang

Published Jan 1, 2005• 0 citations
OpenAlex

Abstract

Quantum genetic algorithm is a product which is combined quantum computation with genetic algorithm;the state vector representation is introduced to the genetic algorithm.Comparing to the genetic algorithm,it has good inquring efficiency and convergence.This paper introduces the principle of quantum genetic algorithm,and discusses on the reforms of the algorithm;then it uses the algorithm in the non-restriction optimization.Some practical examples show the algorithm is effective and feasible.

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This paper explores the application of genetic algorithms (GAs) to optimize quantum circuits. The core claim is that GA can effectively improve the efficiency and accuracy of quantum computation by strategically modifying the topology and control parameters of quantum circuits. The proposed mechanism treats a quantum circuit as a genetic individual, leveraging the principles of genetic algorithms – including crossover, mutation, and selection – to iteratively enhance circuit performance. This approach represents a novel method for quantum circuit optimization, offering a potentially robust and adaptable solution compared to traditional optimization techniques. We present a framework for implementing this approach, focusing on the algorithmic steps and considerations for successful application. The exploration will cover the challenges and potential benefits of integrating evolutionary algorithms with the unique characteristics of quantum computation. ---

•Sep 5, 2026•0 citations
Quantum genetic algorithm is a product which is combined quantum computation with genetic algorithm;the state vector representation is introduced to the genetic algorithm.Comparing to the genetic algorithm,it has good inquring efficiency and convergence.This paper introduces the principle of quantum genetic algorithm,and discusses on the reforms of the algorithm;then it uses the algorithm in the non-restriction optimization.Some practical examples show the algorithm is effective and feasible.
This paper proposes a novel evolutionary computing method called a genetic quantum algorithm (GQA). GQA is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting qubit chromosome as a representation GQA can represent a linear superposition of solutions due to its probabilistic representation. As genetic operators, quantum gates are employed for the search of the best solution. Rapid convergence and good global search capability characterize the performance of GQA. The effectiveness and the applicability of GQA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that GQA is superior to other genetic algorithms using penalty functions, repair methods and decoders.
The quantum genetic algorithm (QGA) is derived from the integration of quantum computation and genetic algorithm. It is characterized with advantages like strong global optimization ability, fast convergence speed and small population size. In this paper, the principle, method and basic flow of quantum genetic algorithm (QGA) are introduced, and the research progress of QGA in recent years is reviewed, including the coding extension of theoretical basis, the innovation of operators, the rotation angle of quantum gates, the optimization of complex high-dimensional functions, and hybrid algorithms, and then he application status of QGA is also discussed. Finally, the future development direction of QGA is put forward.
The genetic algorithm is an efficient optimization tool for its independence of problems, intrinsic parallelism and inherent learning capacity; however, it has some disadvantages such as slow convergence and prematurity. In this paper, a novel algorithm, called the quantum genetic algorithm—QGA, is proposed based on the combination of the quantum theory with the evolutionary theory. By adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. The random observation simulating the quantum collapse can bring diverse individuals, and the evolution of quantum chromosome can also pilot the evolution. So it has better diversity than the classical genetic algorithm. Rapid convergence and good global search capacity characterize the performance of QGA. The paper not only proves the global convergence of the QGA, but also some simulation experiments are shown to prove its superiority over other algorithms.
Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA) and Genetic Quantum Algorithm (GQA). The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.
This paper explores the application of genetic algorithms (GAs) to optimize quantum circuits. The core claim is that GA can effectively improve the efficiency and accuracy of quantum computation by strategically modifying the topology and control parameters of quantum circuits. The proposed mechanism treats a quantum circuit as a genetic individual, leveraging the principles of genetic algorithms – including crossover, mutation, and selection – to iteratively enhance circuit performance. This approach represents a novel method for quantum circuit optimization, offering a potentially robust and adaptable solution compared to traditional optimization techniques. We present a framework for implementing this approach, focusing on the algorithmic steps and considerations for successful application. The exploration will cover the challenges and potential benefits of integrating evolutionary algorithms with the unique characteristics of quantum computation. ---