Optimization of a quantumdot semiconductor optical amplifier. Function optimization based on quantum genetic algorithm ying sun1, yuesheng gu2and hegen xiong1. In the example shown in figure 5, two qubits are declared with qureg. Genetic algorithm, quantum computing, qubit, entanglement, crossover. Genetic quantum algorithm and its application to combinatorial optimization problem abstract. Introduction quantum genetic algorithm qga is the product of the combination of quantum computation and genetic algorithms, and it is a new evolutionary algorithm of probability. In a quantum computer, the population is represented as a probability distribution in a quantum register.
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. The so called quantum evolutionary programming has two major subareas. Abstract recently, researchers have applied genetic algorithms gas to address some problems in quantum computation. Generally speaking, genetic algorithms are simulations of evolution, of what kind ever. Ling wang, hao wu, dazhong zheng, a quantum inspired genetic algorithm for scheduling problems, proceedings of the first international conference on advances in natural computation, august 2729, 2005, changsha, china. Contrary to all qiga algorithms which represent quantum genes as independent qubits, in higherorder qigas quantum registers are used to represent. In the optimization model, the genetic algorithm was used as the basis, the parallelism of quantum computing was integrated with the genetic algorithm, the quantum vector state expression was introduced into the genetic coding, and the chromosome evolution and renewal were achieved through the quantum revolving door. Computer science neural and evolutionary computing. Quantum genetic algorithm qga 1 2 recently proposed can treat the balance between exploration and exploitation more easily when compared with conventional genetic algorithm ga.
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. This paper proposes a novel evolutionary computing method called a genetic quantum algorithm gqa. This has potential uses in a wide range of applications, such as quantum computing or quantum cryptography, where it can be valuable to quickly recognise what type of quantum state has been produced. Genetic algorithm ga provides a common system framework to solve complex. Jan 12, 2016 there is a quantum algorithm which allows any such formula to be evaluated in slightly more than on 12 operations, 52 while it is known that for a wide class of boolean formulae, any randomised. Quantuminspired genetic algorithms applied to ordering. Jan 11, 2020 in this paper, we proposed an intelligence model for the optimal design of the quantum dot semiconductor optical amplifier qdsoa. Comparison of genetic algorithm and quantum genetic algorithm. A fast quantum mechanical algorithm for database search.
Introduction quantum computation is a flourishing research area and quantum computers are believed to hold immense. Genetic programming can automatically discover new algorithms for quantum computers spector et al. This is a paper that preskill cited in his keynote address see yesterdays post. Introduction the optimization of infrastructure has been studied by many researchers, focusing mainly on the optimization during the design phase, but less on the optimization of pipelines operation and maintenance. Backward simulation was performed on the combined simulation platform of matlabsimulink and avlcruise to optimize, simulate, and verify the system parameters of the transmission system. Read online comparison of genetic algorithm and quantum genetic algorithm book pdf free download link book now. The intelligence model is designed using the artificial neural network ann and genetic algorithm ga. A quantuminspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware.
Two of those algorithms will be described in detail in this tutorial in order to better elucidate the study of quantum computing theory and quantum algorithm design. Quantum genetic algorithms for computer scientists mdpi. Optimal placement and sizing of distributed generation using quantum genetic algorithm for reducing losses and improving voltage profile. 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. It operates variables upon a universal quantum chromosome collectively. Applications of quantum computation are widespreading in many different areas, for example quantum genetic algorithms for feature selection 11 or quantum.
This paper presents an original quantum genetic algorithm for multiple sequence alignment qgmalign that combines a genetic algorithm and a quantum algorithm. Multiple sequence alignment by quantum genetic algorithm. Comparison of genetic algorithm and quantum genetic. A swarm optimization genetic algorithm based on quantum. Quantum genetic algorithm qga is the product of the combination of quantum computation and genetic algorithms, and it is a new evolutionary algorithm of probability. The eight colored bars represent individual sets, black bar and dotted line is overall average quantum approach is faster and finds a. Adaptive quantum inspired genetic algorithm for combinatorial optimization problems jyoti chaturvedi d.
The improvement of quantum genetic algorithm and its. In this latter algorithm the onepoint crossover probability was equal to 0. In the dirac notation used in quantum physics, these are denoted 0 and 1. 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. A hybrid machine learning algorithm for designing quantum. To obtain a classic gene from a quantum gene, it is sufficient to observe or randomly select the quantum bit based on the. For example, a quantum system of a single qubit is and.
Obviously, the classical quantum interference crossover is just a position based crossover. Here, we introduce a novel quantum genetic algorithm which has a quantum. In this paper, a realcoded chaotic quantum inspired genetic algorithm rcqga is proposed based on the chaotic and coherent characters of qbits. 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. It can be used for the purposes of education and research. Yu obviously, the population diversity of the new algorithm has larger fluctuation range. Gqa is based on the concept and principles of quantum computing such as qubits and superposition of states.
Chaotic quantum genetic algorithm and its application yang, hongwei dai. Quantum computation is therefore a subject of considerable theoretical interest that may also have practical applications in the future. In classical quantum genetic algorithms, chromosomes are generally represented by two types, qubits and binary, during the algorithm procedure. Genetic algorithms and quantum computation gilson a. Genetic algorithms gas were invented by john holland in the 1960s and were developed by holland and his students and colleagues at the university of michigan in the.
Over the last decade, the possibility to emulate a quantum computer a computer using quantum. In quantum computing, a quantum algorithm is an algorithm which runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. Quantum genetic algorithms for computer scientists rafael lahozbeltra department of applied mathematics biomathematics, faculty of biological sciences, complutense university of madrid, madrid 28040, spain. Revenue optimization of pipelines construction and operation. A classical or non quantum algorithm is a finite sequence of instructions, or a stepbystep procedure for solving a problem, where each step or instruction can be performed on a classical computer. Genetic algorithms gas are a class of evolutionary algorithms inspired by darwinian natural selection. Jul 19, 2000 genetic quantum algorithm and its application to combinatorial optimization problem abstract. Fundamental notions of the theory have been introduced, and a novel order2 quantum inspired genetic algorithm qiga2 has been developed.
A quantum probabilistic coding is designed for representing the multiple sequence alignment. To begin, we start with n quantum registers, labeled reg1 0 through reg1 n1, where n will be the population size. Analysis on the characteristic of quantum genetic algorithm. Application of genetic algorithms for evolution of quantum equivalents of boolean circuits. It covers some of the same ground that we looked at yesterday, but also has some additional material and perspective of interest and ill focus on those parts today. Mar 23, 2019 download comparison of genetic algorithm and quantum genetic algorithm book pdf free download link or read online here in pdf. Multilevel edge detection using quantum and classical. Our hybrid algorithm, utilising a genetic algorithm to. Over the last decade, the possibility to emulate a quantum computer a computer using quantum mechanical phenomena to perform operations on data has led to a new class of gas known as quantum genetic algorithms qgas. Quantum genetic algorithms for computer scientists core. The combinatorial optimization problem is aimed at finding an optimal object from a finite set of objects. To pick up the desired component, one must be able to identify it uniquely. In this section we present the classical and the quantum genetic algorithms that we will use in our experiments. Mar 04, 2004 the so called quantum evolutionary programming has two major subareas.
An algorithm for synthesis of quantum reversible logic. Many of these algorithms generalize the main idea of shors algorithm. Many proposed quantum computer technologies have the feature that quantum bits, or qubits, are stored in speci. Research article the improvement of quantum genetic algorithm. Genetic quantum algorithms in the new study, the researchers demonstrated that genetic algorithms can identify gate designs for digital quantum simulations that outperform designs identified by standard optimization techniques, resulting in the lowest levels of digital quantum errors achieved so far. In most cases, however, genetic algorithms are nothing else than probabilistic optimization methods which are based on the principles of evolution. Pdf quantum genetic algorithms for computer scientists. Inpart i, we discuss quantum circuitsin particular, the problem of expressing a quantum algorithm using a given universal set of quantum gates. For combinatorial optimization, the quantum approximate optimization algorithm qaoa briefly had a better approximation ratio than any known polynomial time classical algorithm for a certain problem, until a more effective classical algorithm was proposed.
Typical approaches to designing quantum genetic algorithms are. Multiobjective construction optimization model based on. Quantum genetic algorithms for computer scientists. Function optimization based on quantum genetic algorithm. India abstract the development in the field of quantum computing gives us a significant edge over classical computing in terms of time and efficiency.
This paper presents a new methodology for running genetic algorithms on a quantum computer. A quantum inspired genetic algorithm based optimization method for mobile impact test data integration 4 continuous rigidframe bridge are studied respectively to verify the effectiveness of the proposed method. Quantum computation is the eld that investigates the computational power and other properties of computers based on quantum mechanical principles. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. However, another alternative of evolutionary algorithms was introduced. A quantum genetic algorithm for pickup and delivery problems. Genetic algorithms which has been designed with appropriate representation and search operations towards faster convergence.
All books are in clear copy here, and all files are secure so dont worry about it. For example, a 2phase heuristic was proposed in 11, and a branchandcut algorithm for 2index pdptw 15. In this paper we exhibit the first evolved better thanclassical quantum algorithm, for deutschs early promise problem. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Computers free fulltext quantum genetic algorithms. Thus it can adjust evolutionary intensity dynamically and consider evolutionary. This approach started in the 1970s by john holland. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i. Through the construction example of a highrise building project, the quantum genetic algorithm was proved to be able to obtain the optimization results under the condition of small population size and few iteration times. After the role of some quantum gate, the values of and. The relative speedup of the quantum algorithm is an open research question. A quantum genetic algorithm with quantum crossover and mutation. This is a variant of the unsorted database search problem, unsorted because prior to their selection the components are not arranged in any particular order.
But the superposition principle says that, in fact, the electron is in a state that is a linear combination of these two. Quantum inspired genetic algorithms qigas and quantum genetic algorithms qgas. In a classical computer, the compact genetic algorithm represents the population as a probability distribution over the set of solutions by using a vector. Among those techniques, genetic algorithm ga techniques try to. Inpart ii, we discuss quantum algorithms for algebraic problems. In qpso, each particle has a target point, which is defined as a linear combination of the best previous position of the particle and the global best position.
The required data for training the ann are collected from the numerical. Accepted manuscript designing quantum experiments with a. Realcoded chaotic quantuminspired genetic algorithm for. Recently, researchers have applied genetic algorithms gas to address some problems in quantum computation. This is a set of lecture notes on quantum algorithms. Introduction quantum computation is a flourishing research area and.
In 1996, quantum genetic algorithm is first proposed by narayanan and moore, and it is successfully used to solve the tsp problem. The improvement of quantum genetic algorithm and its application. In this paper we describe a new approach for the well known problem in bioinformatics. Quantum genetic algorithm, simulated annealing algorithm, pipelines construction management, operation optimization 1. Genetic algorithms ga are a representative example of a set of methods known as evolutionary algorithms. The compact genetic algorithm cga 4 is one of them. Genetic programming can be used to automatically discover algorithms for quantum computers that are more efficient than any classical computer algorithms for the same problems. We first present a simple, accurate, and fast model based on the feedforward ann. Pdf optimal placement and sizing of distributed generation. May 25, 2017 inspired by the quantum theory and the trajectory analysis of pso, sun et al. Even if use genetic algorithm, because the length of chromosome which is used to represent quantum gate sets is longer, it will inevitably reduce the effectiveness of the genetic algorithm, so within a limited time in a limited space it cannot get a final solution. Compiling quantum programs using genetic algorithms. Research article powertrain matching and optimization of. A quantum genetic algorithm with quantum crossover and mutation operations.
It can not be expected to reduce the current tour length. The rst algorithm that will be explored in this tutorial is lov grovers quantum database search 5. These notes were prepared for a course that was offered at the university of waterloo in 2008, 2011, and 20, and at the university of maryland in 2017. As a result, the new algorithm has a higher probability to search better solutions in the solution space than old algorithm. Binary chromosomes are generated by observing equating quantum collapsing in quantum mechanics qubit chromosomes. In this paper we exhibit the first evolved betterthanclassical quantum algorithm, for deutschs early promise problem. Quantum genetic algorithm based on qutrits and its application. This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. Quantum algorithms and the genetic code up one by one and arranged in the required order. Hybrid genetic algorithm hga is a ga that combines quantum operators rotation, measure, quantum chromosomes, etc. 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. Thus, the quantum genetic algorithm had the advantages of short calculation time and strong global optimization ability.
Pdf recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers for some. We are exploring the use of genetic algorithms as one method for the storage assignment and gate execution location assignment phase of a compiler back end. For example two parent chromosomes 110100110110 and. Chaotic quantum genetic algorithm and its application. This paper demonstrates an alternative way to program a quantum computer to perform compact genetic algorithms. Chaotic quantum genetic algorithm and its application yang, hongwei dai, cunhua li 262. It is well known, however, that the searching speed of the conventional quantum genetic algorithms qga is not satisfactory.
The simulation experiments conducted with above quantum genetic algorithms were compared with a non quantum simple genetic algorithm sga. Evolution of quantum teleportation circuits with improved. Keywords quantum teleportation, quantum circuit, entanglement, adjacent gates, genetic algorithm. Knott 1, 1 centre for the mathematics and theoretical physics of quantum nonequilibrium systems cqne. An important objective is to nd quantum algorithms that are signi cantly faster than any classical algorithm solving the same problem. To the best of our knowledge and according to reference 6there are no feasible solutions for the implementation of the quantum genetic algorithms qgas. Msa is fundamental task as it represents an essential platform to conduct other tasks in bioinformatics such as the construction of phylogenetic trees, the. Computers free fulltext quantum genetic algorithms for. Designing quantum experiments with a genetic algorithm rosanna nichols, 1 lana mineh, 2,3 jesus rubio, 4 jonathan c. Quantum simulator 14 purely classical genetic algorithm simulated quantum prune and anneal an 80% coveragered is the minimum acceptable average.
Many algorithms are performed to multilevel thresholding segmentation to improve the com putational efficiency. An implementation of compact genetic algorithm on a quantum. These two algorithms are good models for our current understanding of quantum computation as many other quantum algorithms. A quantum genetic algorithm for pickup and delivery problems with. The hybrid strategy enables this algorithm not only to share with some common operations like crossover and mutation in classical ga, but also to have quantum characteristics such as quantum rotation gate and measurement of collapse. Aiming to this problem, an improved quantum crossover is proposed. These algorithms use the quantum fourier transform and typically. Quantum genetic algorithms qga are typically built using the traditional representation of thequantum chromosome in the form of system of independent qubits. Now for the first time, researchers urtzi las heras et al. A quantum genetic algorithm to solve the problem of multivariate. The tutorial also illustrates genetic search by hyperplane sampling. The former adopts qubit chromosomes as representations and employs quantum gates for the search of the best solution. Pdf application of genetic algorithms for evolution of. An implementation of compact genetic algorithm on a.1301 210 455 928 1303 178 196 1005 629 1316 590 402 864 286 664 1246 758 505 1293 802 63 1436 1088 68 341 744 237 1237