The smallest unit of information stored in a twostate quantum computer is called a quantum bit or qubit 6. This paper proposes a novel evolutionary computing method called a genetic quantum algorithm gqa. Quantum genetic algorithms for computer scientists mdpi. Cryptanalysis of tea using quantuminspired genetic. Gpubased tuning of quantuminspired genetic algorithm. Gpubased tuning of quantuminspired genetic algorithm for. It can be used for the purposes of education and research.
Pdf quantum inspired genetic algorithm and particle. The algorithm presented in this paper contributes to the quantuminspired genetic approach to solve ordering combinatorial optimization problems. The improvement of quantum genetic algorithm and its. An important consequence of this work is to emphasize. Previous work in the field of applying evolutionary algorithms to quantum computation there are two major directions. The novel contribution of the proposed lsqea is the use of a. Of course, searching for an unique element in an unordered set can be generalized to apply to a wide variety of problems in computer science, such as the problem of boolean satis ability sat. Orderr quantuminspired genetic algorithms are ca pable of modelling relations between separate genes which are joined into the same quantum register of size r. Pdf a quantuminspired genetic algorithm for data clustering. Genetic algorithms gas are a class of evolutionary algorithms inspired by darwinian natural selection. Figure 2 illustrates an example of the convergence speed for function 1 of table 1 using all the evolutionary algorithms. In fact, grovers algorithm is optimal for any quantum algorithm for performing such a search 6. Evaluation, hybridization and application of quantum.
Pdf this paper proposes a new parallel evolutionary algorithm called parallel quantuminspired genetic algorithm pqga. Isnt there a simple solution we learned in calculus. Quantuminspired evolutionary approach for the quadratic. Here qiga, the representation of the population individual is inspired by the concept of qbit in quantum computing.
This paper proposes a new perspective on qgas, by showing that no genetic algorithm strategy is necessary in. In 11 we found another quantum inspired genetic algorithm qiga which relies on usual methods of quantum mechanics. The quantum monte carlo quantum annealing qmcqa 1 or discretetime simulated quantum annealing sqa 2 algorithms performed better than the tested dwave device in recent studies we establish the first example of a scaling advantage for an experimental quantum annealer over classical simulated annealing. The structure of qga is described in the following pseudo code. Gpubased tuning of quantuminspired genetic algorithm for a combinatorial optimization problem. An introduction to genetic algorithms melanie mitchell. Instead of binary, numeric, or symbolic represen tation, by adopting qubit chromosome as a representa. Narayanan and moore 6 proposed quantum inspired genetic algorithms, where concepts and principles of quantum mechanics are used to inform and inspire more ef.
In this paper, the second approach has been taken, and qiga algorithm has been implemented entirely as a computational kernel running on gpu. Many proposed quantum computer technologies have the feature that quantum bits, or qubits, are stored in speci. In the example shown in figure 5, two qubits are declared with qureg. Thus, quantum circuit design is more like classical program compilation than circuit.
Fundamental notions of the theory have been introduced, and a novel order2 quantum inspired genetic algorithm qiga2 has been presented. Double chain coding in the dcqga, the probability amplitude of quantum bits is used for coding and the double chain coding is proposed. Function optimization based on quantum genetic algorithm. List of quantuminspired algorithms theoretical computer. It is characterized by principles of quantum computing, including concepts of qubits and superposition of states, as well as quantum operators to improve convergence. The idea behind quantuminspired genetic algorithms is to generate each classic individual based on the quantum individual. Quantuminspired acromyrmex evolutionary algorithm scientific. Genetic quantum algorithm and its application to combinatorial optimization problem abstract. The rst algorithm that will be explored in this tutorial is lov grovers quantum database search 5. Quantuminspired genetic algorithm qga has a better characteristic of population diversity, rapid convergence and global search capability than that of the conventional genetic algorithm cga.
Generating supply chain ordering policies using quantum. Quantuminspired genetic algorithms applied to ordering. A quantum inspired genetic algorithm for multimodal. Building blocks propagation in quantuminspired genetic. An introduction to genetic algorithms jenna carr may 16, 2014 abstract genetic algorithms are a type of optimization algorithm, meaning they are used to nd the maximum or minimum of a function. Option pricing model calibration using a realvalued. This motivated researchers to take advantage of the quantum computational parallelism and integrate it into the evolutionary framework. Quantum inspired genetic optimization algorithm for flight control parameter tuning 3. Higherorder quantuminspired genetic algorithms arxiv. Newtonraphson and its many relatives and variants are based on the use of local information. Generating supply chain ordering policies using quantum inspired genetic algorithms and grammatical evolution. Instead of bits, quantum genes are modelled upon the concept.
Grovers algorithm and the evolutionary approach of. All quantuminspired genetic algorithms that use independent qubits to represent binary genes are order1. In essence, in qigas the traditional representations. Quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for gray level image thresholding. Gqa is based on the concept and principles of quantum computing such as qubits and superposition of states.
Quantum inspired genetic algorithm qiga in qiga some of the features of quantum computing are implemented with the concepts of genetic algorithm 12. Quantuminspired wolf pack algorithm to solve the 01. Quantuminspired genetic al gorithmqga is based on the concept and principles of quantum computing such as qubits and superposition of states. A new algorithm, the immune quantuminspired genetic algorithm iqga, is proposed by introducing immune concepts and methods into quantuminspired. Grovers algorithm searches for a speci ed entry in an unordered database, employing an important technique in quantum algorithm design known as amplitude ampli cation to achieve a polynomial speedup over the best classical algorithms. Pdf parallel quantuminspired genetic algorithm for. In order to make sure the performance of the fcs satisfies the cruising phase demands, a combination of performance requirements is given in eq. Research in the area of quantum genetic algorithms has been started in late 1990s and. Analysis on the characteristic of quantum genetic algorithm. An improved quantuminspired evolutionary algorithm is proposed for solving mixed discretecontinuous nonlinear problems in engineering design. Considering the randomness of initial population and the. The first step enables the population to move to the global optima and the second step helps to avoid the trapping of.
For example, quantum genetic optimization algorithm 3 has introduced an optimization of classical genetic algorithm using the principles of quantum search which provided a significant speedup on each genetic step. The qiga is based on the concepts of a qubit quantum bit and the superposition of states. A novel evolutionary computing methodquantum inspired genetic algorithmsis introduced, where concepts and principles of quantum mechanics are used to inform and inspire more efficient evolutionary computing methods. A quantuminspired genetic algorithmbased optimization method for mobile impact test data integration 4 continuous rigidframe bridge are studied respectively to verify the effectiveness of the proposed method. These algorithms are inspired by certain concept and principles of quantum computing such as qubits and superposition of states. A quantum inspired genetic algorithm for multimodal optimization of. We think that the most illustrative example of qea is the algorithm firstly. The novelty of the proposed method is that the quantum. This paper presents a theory and an empirical evaluation of higherorder quantuminspired genetic algorithms. We show what components make up genetic algorithms and how. A novel quantuminspired genetic algorithm with expanded. An implementation of compact genetic algorithm on a. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i. However, the solutions of qgas may diverge or have a premature convergence to a local optimum due to the selection of the rotation angle in searching.
Quantuminspired genetic algorithms ieee conference. Chromosomes in these algorithms are probabilistically represented by qubits. In the case of quantuminspired genetic algorithms applied to the combinatorial optimization problems, each gene of an individual is represented by a quantum bit. The quantum inspired genetic algorithm the bestknown application of quantum inspired concepts in evolutionary computing is the quantum inspired genetic algorithm qiga 16, 20, 21. Quantum inspired evolutionary algorithm for ordering. For example, the geometric reduction of the qgate angle expressed as. Quantum genetic algorithm is a more wonderful optimization process than the conventional genetic algorithm, and its encoding mode is more complex, and each generation of the evolution can cover a wider area. In this paper, as an enhancement to quantuminspired genetic algorithms qiga, we propose two supportive search schemes tsss, i.
We think that the most illustrative example of qea is the algorithm firstly proposed by han and kim in 7 where some major principles of quantum computing are. Algorithm 1 order2 quantuminspired genetic algorithm 1. A quantuminspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. Pdf quantum genetic algorithms for computer scientists. This paper proposes a new quantum inspired evolutionary algorithm for solving ordering problems. The basic terminology of quantum mechanics is introduced before a comparison is made between a classical genetic algorithm and a quantum inspired method for the travelling salesperson problem. Pdf quantuminspired genetic algorithms researchgate. Quantuminspired evolutionary algorithms qiea 5, 6 7 are a new class of evolutionary algorithms which are inspired. Thus, for example, in an algorithm coding solutions in the. The proposed latin square quantuminspired evolutionary algorithm lsqea combines latin squares and quantuminspired genetic algorithm qga. In this paper we introduce, illustrate, and discuss genetic algorithms for beginning users. The function value and the derivatives with respect to the parameters optimized are used to take a step in an appropriate direction towards a local. Multithreshold image segmentation with improved quantum. Quantum inspired evolutionary algorithms based on binary and real representations have been previously developed to solve combinatorial and numerical optimization problems, providing better results than classical genetic algorithms with less computational effort.
Compiling quantum programs using genetic algorithms. Quantum genetic algorithm combines some characteristics of quantum computation with the genetic algorithm whose. Pdf parallel quantuminspired genetic algorithm for combinatorial. In qiga algorithm, a novel representation of solutions, binary quantum coding, is employed.
Contributions to a supply chains overall cost function such as the bullwhip effect are sensitive to the different players ordering policies. A versatile quantuminspired evolutionary algorithm core. The quantum algorithm provides a quadratic improvement over the best classical algorithm in the general case, and an exponential improvement when the input matrices are of low rank. Hybrid genetic algorithm hga is a ga that combines quantum operators rotation, measure, quantum chromosomes, etc. Reduced quantum genetic algorithm rqga is a program in python showing how to implement a true quantum genetic algorithm based on a fitness quantum gate and grovers search algorithm.
Han and kim17 proposed a quantuminspired evolutionary algorithm based. Han and kim16 applied the quantum state vector table into the genetic codes for adjusting the chromosome by quantum rotation. A novel immune quantuminspired genetic algorithm springerlink. Improved quantuminspired evolutionary algorithm for. This paper proposes a quantuminspired wolf pack algorithm qwpa based on quantum encoding to enhance the performance of the wolf pack algorithm wpa to solve the 01 knapsack problems. Quantuminspired genetic algorithm qga is based on the concept and principles. This paper develops a quantuminspired genetic algorithm qga to find the sets of optimal parameters for the wind disturbance alleviation flight control. Quantuminspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention.