Relevant bibliographies by topics / Quantum inspired algorithms

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Quantum inspired algorithms'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Quantum inspired algorithms"

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ZHANG, Yi, Kai LU, and Ying-Hui GAO. "Quantum Algorithms and Quantum-Inspired Algorithms." Chinese Journal of Computers 36, no. 9 (2014): 1835–42. http://dx.doi.org/10.3724/sp.j.1016.2013.01835.

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Hezam, Ibrahim M., Osama Abdul-Raof, Abdelaziz Foul, and Faisal Aqlan. "A Quantum-Inspired Sperm Motility Algorithm." AIMS Mathematics 7, no. 5 (2022): 9057–88. http://dx.doi.org/10.3934/math.2022504.

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<abstract> <p>Sperm Motility Algorithm (SMA), inspired by the human fertilization process, was proposed by Abdul-Raof and Hezam <sup>[<xref ref-type="bibr" rid="b1">1</xref>]</sup> to solve global optimization problems. Sperm flow obeys the Stokes equation or the Schrۤinger equation as its derived equivalent. This paper combines a classical SMA with quantum computation features to propose two novel Quantum-Inspired Evolutionary Algorithms: The first is called the Quantum Sperm Motility Algorithm (QSMA), and the second is called the Improved Quantum Sperm Motility Algorithm (IQSMA). The IQSMA is based on the characteristics of QSMA and uses an interpolation operator to generate a new solution vector in the search space. The two proposed algorithms are global convergence guaranteed population-based optimization algorithms, which outperform the original SMA in terms of their search-ability and have fewer parameters to control. The two proposed algorithms are tested using thirty-three standard dissimilarities benchmark functions. Performance and optimization results of the QSMA and IQSMA are compared with corresponding results obtained using the original SMA and those obtained from three state-of-the-art metaheuristics algorithms. The algorithms were tested on a series of numerical optimization problems. The results indicate that the two proposed algorithms significantly outperform the other presented algorithms.</p> </abstract>
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Arrazola, Juan Miguel, Alain Delgado, Bhaskar Roy Bardhan, and Seth Lloyd. "Quantum-inspired algorithms in practice." Quantum 4 (August 13, 2020): 307. http://dx.doi.org/10.22331/q-2020-08-13-307.

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We study the practical performance of quantum-inspired algorithms for recommendation systems and linear systems of equations. These algorithms were shown to have an exponential asymptotic speedup compared to previously known classical methods for problems involving low-rank matrices, but with complexity bounds that exhibit a hefty polynomial overhead compared to quantum algorithms. This raised the question of whether these methods were actually useful in practice. We conduct a theoretical analysis aimed at identifying their computational bottlenecks, then implement and benchmark the algorithms on a variety of problems, including applications to portfolio optimization and movie recommendations. On the one hand, our analysis reveals that the performance of these algorithms is better than the theoretical complexity bounds would suggest. On the other hand, their performance as seen in our implementation degrades noticeably as the rank and condition number of the input matrix are increased. Overall, our results indicate that quantum-inspired algorithms can perform well in practice provided that stringent conditions are met: low rank, low condition number, and very large dimension of the input matrix. By contrast, practical datasets are often sparse and high-rank, precisely the type that can be handled by quantum algorithms.
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Zhang, Rui, Zhiteng Wang, and Hongjun Zhang. "Quantum-Inspired Evolutionary Algorithm for Continuous Space Optimization Based on Multiple Chains Encoding Method of Quantum Bits." Mathematical Problems in Engineering 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/620325.

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This study proposes a novel quantum evolutionary algorithm called four-chain quantum-inspired evolutionary algorithm (FCQIEA) based on the four gene chains encoding method. In FCQIEA, a chromosome comprises four gene chains to expand the search space effectively and promote the evolutionary rate. Different parameters, including rotational angle and mutation probability, have been analyzed for better optimization. Performance comparison with other quantum-inspired evolutionary algorithms (QIEAs), evolutionary algorithms, and different chains of QIEA demonstrates the effectiveness and efficiency of FCQIEA.
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Sahni, Srishti, Vaibhav Aggarwal, Ashish Khanna, Deepak Gupta, and Siddhartha Bhattacharyya. "Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution." Journal of information and organizational sciences 44, no. 2 (2020): 345–63. http://dx.doi.org/10.31341/jios.44.2.9.

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Parkinson’s Disease is a degenerative neurological disorder with unknown origins, making it impossible to be cured or even diagnosed. The following article presents a Three-Layered Perceptron Neural Network model that is trained using a variety of evolutionary as well as quantum-inspired evolutionary algorithms for the classification of Parkinson's Disease. Optimization algorithms such as Particle Swarm Optimization, Artificial Bee Colony Algorithm and Bat Algorithm are studied along with their quantum-inspired counter-parts in order to identify the best suited algorithm for Neural Network Weight Distribution. The results show that the quantum-inspired evolutionary algorithms perform better under the given circumstances, with qABC offering the highest accuracy of about 92.3%. The presented model can be used not only for disease diagnosis but is also likely to find its applications in various other fields as well.
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HUO, HONG-WEI, VOJISLAV STOJKOVIC, and QIAO-LUAN XIE. "A QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON PROBABILISTIC CODING FOR MULTIPLE SEQUENCE ALIGNMENT." Journal of Bioinformatics and Computational Biology 08, no. 01 (2010): 59–75. http://dx.doi.org/10.1142/s0219720010004549.

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Quantum parallelism arises from the ability of a quantum memory register to exist in a superposition of base states. Since the number of possible base states is 2n, where n is the number of qubits in the quantum memory register, one operation on a quantum computer performs what an exponential number of operations on a classical computer performs. The power of quantum algorithms comes from taking advantages of quantum parallelism. Quantum algorithms are exponentially faster than classical algorithms. Genetic optimization algorithms are stochastic search algorithms which are used to search large, nonlinear spaces where expert knowledge is lacking or difficult to encode. QGMALIGN — a probabilistic coding based quantum-inspired genetic algorithm for multiple sequence alignment is presented. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The experimental results show that QGMALIGN can compete with the popular methods, such as CLUSTALX and SAGA, and performs well on the presenting biological data. Moreover, the addition of genetic operators to the quantum-inspired algorithm lowers the cost of overall running time.
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Gilyén, András, Zhao Song, and Ewin Tang. "An improved quantum-inspired algorithm for linear regression." Quantum 6 (June 30, 2022): 754. http://dx.doi.org/10.22331/q-2022-06-30-754.

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We give a classical algorithm for linear regression analogous to the quantum matrix inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters'09] for low-rank matrices [Wossnig, Zhao, and Prakash, Physical Review Letters'18], when the input matrix A is stored in a data structure applicable for QRAM-based state preparation.Namely, suppose we are given an A∈Cm×n with minimum non-zero singular value σ which supports certain efficient ℓ2-norm importance sampling queries, along with a b∈Cm. Then, for some x∈Cn satisfying ‖x–A+b‖≤ε‖A+b‖, we can output a measurement of |x⟩ in the computational basis and output an entry of x with classical algorithms that run in O~(‖A‖F6‖A‖6σ12ε4) and O~(‖A‖F6‖A‖2σ8ε4) time, respectively. This improves on previous "quantum-inspired" algorithms in this line of research by at least a factor of ‖A‖16σ16ε2 [Chia, Gilyén, Li, Lin, Tang, and Wang, STOC'20]. As a consequence, we show that quantum computers can achieve at most a factor-of-12 speedup for linear regression in this QRAM data structure setting and related settings. Our work applies techniques from sketching algorithms and optimization to the quantum-inspired literature. Unlike earlier works, this is a promising avenue that could lead to feasible implementations of classical regression in a quantum-inspired settings, for comparison against future quantum computers.
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Melucci, Massimo. "Relevance Feedback Algorithms Inspired By Quantum Detection." IEEE Transactions on Knowledge and Data Engineering 28, no. 4 (2016): 1022–34. http://dx.doi.org/10.1109/tkde.2015.2507132.

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Guo, Yi-nan, Pei Zhang, Jian Cheng, Chun Wang, and Dunwei Gong. "Interval multi-objective quantum-inspired cultural algorithms." Neural Computing and Applications 30, no. 3 (2016): 709–22. http://dx.doi.org/10.1007/s00521-016-2572-5.

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Majima, Kei, and Naoko Koide-Majima. "Quantum-Inspired Algorithms for Accelerating Machine Learning." Brain & Neural Networks 29, no. 4 (2022): 186–92. http://dx.doi.org/10.3902/jnns.29.186.

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Dissertations / Theses on the topic "Quantum inspired algorithms"

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CRUZ, ANDRE VARGAS ABS DA. "QUANTUM-INSPIRED EVOLUTIONARY ALGORITHMS FOR PROBLEMS BASED ON NUMERICAL REPRESENTATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10640@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Desde que foram propostos como método de otimização, os algoritmos evolutivos têm sido usados com sucesso para resolver problemas complexos nas mais diversas áreas como, por exemplo, o projeto automático de circuitos e equipamentos, planejamento de tarefas, engenharia de software e mineração de dados, entre tantos outros. Este sucesso se deve, entre outras coisas, ao fato desta classe de algoritmos não necessitar de formulações matemáticas rigorosas a respeito do problema que se deseja otimizar, além de oferecer um alto grau de paralelismo no processo de busca. No entanto, alguns problemas são computacionalmente custosos no que diz respeito à avaliação das soluções durante o processo de busca, tornando a otimização por algoritmos evolutivos um processo lento para situações onde se deseja uma resposta rápida do algoritmo (como por exemplo, problemas de otimização online). Diversas maneiras de se contornar este problema, através da aceleração da convergência para boas soluções, foram propostas, entre as quais destacam-se os Algoritmos Culturais e os Algoritmos Co-Evolutivos. No entanto, estes algoritmos ainda têm a necessidade de avaliar muitas soluções a cada etapa do processo de otimização. Em problemas onde esta avaliação é computacionalmente custosa, a otimização pode levar um tempo proibitivo para alcançar soluções ótimas. Este trabalho propõe um novo algoritmo evolutivo para problemas de otimização numérica (Algoritmo Evolutivo com Inspiração Quântica usando Representação Real - AEIQ- R), inspirado no conceito de múltiplos universos da física quântica, que permite realizar o processo de otimização com um menor número de avaliações de soluções. O trabalho apresenta a modelagem deste algoritmo para a solução de problemas benchmark de otimização numérica, assim como no treinamento de redes neurais recorrentes em problemas de aprendizado supervisionado de séries temporais e em aprendizado por reforço em tarefas de controle. Os resultados obtidos demonstram a eficiência desse algoritmo na solução destes tipos de problemas.<br>Since they were proposed as an optimization method, the evolutionary algorithms have been successfully used for solving complex problems in several areas such as, for example, the automatic design of electronic circuits and equipments, task planning and scheduling, software engineering and data mining, among many others. This success is due, among many other things, to the fact that this class of algorithms does not need rigorous mathematical formulations regarding the problem to be optimized, and also because it offers a high degree of parallelism in the search process. However, some problems are computationally intensive when it concerns the evaluation of solutions during the search process, making the optimization by evolutionary algorithms a slow process for situations where a quick response from the algorithm is desired (for instance, in online optimization problems). Several ways to overcome this problem, by speeding up convergence time, were proposed, including Cultural Algorithms and Coevolutionary Algorithms. However, these algorithms still have the need to evaluate many solutions on each step of the optimization process. In problems where this evaluation is computationally expensive, the optimization might take a prohibitive time to reach optimal solutions. This work proposes a new evolutionary algorithm for numerical optimization problems (Quantum- Inspired Evolutionary Algorithm for Problems based on Numerical Representation - QIEA-R), inspired in the concept of quantum superposition, which allows the optimization process to be carried on with a smaller number of evaluations. The work presents the modelling for this algorithm for solving benchmark numerical optimization problems, and for training recurrent neural networks in supervised learning and reinforcement learning. The results show the good performance of this algorithm in solving these kinds of problems.
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CABRI, ALBERTO. "Quantum inspired approach for early classification of time series." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/991085.

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Is it possible to apply some fundamental principles of quantum-computing to time series classification algorithms? This is the initial spark that became the research question I decided to chase at the very beginning of my PhD studies. The idea came accidentally after reading a note on the ability of entanglement to express the correlation between two particles, even far away from each other. The test problem was also at hand because I was investigating on possible algorithms for real time bot detection, a challenging problem at present day, by means of statistical approaches for sequential classification. The quantum inspired algorithm presented in this thesis stemmed as an evolution of the statistical method mentioned above: it is a novel approach to address binary and multinomial classification of an incoming data stream, inspired by the principles of Quantum Computing, in order to ensure the shortest decision time with high accuracy. The proposed approach exploits the analogy between the intrinsic correlation of two or more particles and the dependence of each item in a data stream with the preceding ones. Starting from the a-posteriori probability of each item to belong to a particular class, we can assign a Qubit state representing a combination of the aforesaid probabilities for all available observations of the time series. By leveraging superposition and entanglement on subsequences of growing length, it is possible to devise a measure of membership to each class, thus enabling the system to take a reliable decision when a sufficient level of confidence is met. In order to provide an extensive and thorough analysis of the problem, a well-fitting approach for bot detection was replicated on our dataset and later compared with the statistical algorithm to determine the best option. The winner was subsequently examined against the new quantum-inspired proposal, showing the superior capability of the latter in both binary and multinomial classification of data streams. The validation of quantum-inspired approach in a synthetically generated use case, completes the research framework and opens new perspectives in on-the-fly time series classification, that we have just started to explore. Just to name a few ones, the algorithm is currently being tested with encouraging results in predictive maintenance and prognostics for automotive, in collaboration with University of Bradford (UK), and in action recognition from video streams.
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PINHO, ANDERSON GUIMARAES DE. "QUANTUM-INSPIRED EVOLUCIONARY ALGORITHM WITH MIXED REPRESENTATION APPLIED TO NEURO-EVOLUTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17224@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>Esta dissertação objetivará a unificação de duas metodologias de algoritmos evolutivos consagradas para tratamento de problemas ou do tipo combinatórios, ou do tipo numéricos, num único algoritmo com representação mista. Trata-se de um algoritmo evolutivo inspirado na física quântica com representação mista binário-real do espaço de soluções, o AEIQ-BR. Este algoritmo trata-se de uma extensão do modelo com representação binária de Jang, Han e Kin, o AEIQ-B para otimizações combinatoriais, e o de representação real de Abs da Cruz, o AEIQ-R para otimizações numéricas. Com fins de exemplificação do novo algoritmo proposto, o discutiremos no contexto de neuroevolução, com o propósito de configurar completamente uma rede neural com alimentação adiante em termos: seleção de variáveis de entrada; números de neurônios na camada escondida; todos os pesos existentes; e tipos de funções de ativação de cada neurônio. Esta finalidade em se aplicar o algoritmo AEIQ-BR à neuroevolução – e também, numa analogia ao modelo NEIQ-R de Abs da Cruz – receberá a denominação NEIQ-BR. N de neuroevolução, E de evolutivo, IQ de inspiração quântica, e BR de binário-real. Para avaliar o desempenho do NEIQ-BR, utilizarse- á um total de seis casos benchmark de classificação, e outros dois casos reais, em campos da ciência como: finanças, biologia e química. Resultados serão comparados com algoritmos de outros pesquisadores e a modelagem manual de redes neurais, através de medidas de desempenho. Através de testes estatísticos concluiremos que o algoritmo NEIQ-BR apresentará um desempenho significativo na obtenção de previsões de classificação por neuroevolução.<br>This work aimed to unify two methodologies of evolutionary algorithms to treat problems with or combinatorial characteristics, or numeric, on a unique algorithm with mix representation. It is an evolutionary algorithm inspired in quantum physics with mixed representation of the solutions space, called QIEABR. This algorithm is an extension of the model with binary representation of the chromosome from Jang, Han e Kin, the QIEA-B for combinatorial optimization, and numeric representation from Abs da Cruz, the QIEA-R for numerical optimizations. For purposes of exemplification of the new algorithm, we will introduce the algorithm in the context of neuro-evolution, in order to completely configure a feed forward neural network in terms of: selection of input variables; numbers of neurons in the hidden layer; all existing synaptic weights; and types of activation functions of each neuron. This purpose when applying the algorithm QIEA-BR to neuro-evolution receive the designation of QIEN-BR. QI for quantum-inspired, E for evolutive, N for neuro-evolution, and BR for binary-real representation. To evaluate the performance of QIEN-BR, we will use a total of six benchmark cases of classification, and two real cases in fields of science such as finance, biology and chemistry. Results will be compared with algorithms of other researchers and manual modeling of neural networks through performance measures. Statistical tests will be provided to elucidate the significance of results, and what we can conclude is that the algorithm QIEN-BR better performance others researchers in terms of classification prediction.
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Schliebs, Stefan. "Heterogeneous probabilistic models for optimisation and modelling of evolving spiking neural networks." AUT University, 2010. http://hdl.handle.net/10292/963.

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This thesis proposes a novel feature selection and classification method employing evolving spiking neural networks (eSNN) and evolutionary algorithms (EA). The method is named the Quantum-inspired Spiking Neural Network (QiSNN) framework. QiSNN represents an integrated wrapper approach. An evolutionary process evolves appropriate feature subsets for a given classification task and simultaneously optimises the neural and learning-related parameters of the network. Unlike other methods, the connection weights of this network are determined by a fast one-pass learning algorithm which dramatically reduces the training time. In its core, QiSNN employs the Thorpe neural model that allows the efficient simulation of even large networks. In QiSNN, the presence or absence of features is represented by a string of concatenated bits, while the parameters of the neural network are continuous. For the exploration of these two entirely different search spaces, a novel Estimation of Distribution Algorithm (EDA) is developed. The method maintains a population of probabilistic models specialised for the optimisation of either binary, continuous or heterogeneous search spaces while utilising a small and intuitive set of parameters. The EDA extends the Quantum-inspired Evolutionary Algorithm (QEA) proposed by Han and Kim (2002) and was named the Heterogeneous Hierarchical Model EDA (hHM-EDA). The algorithm is compared to numerous contemporary optimisation methods and studied in terms of convergence speed, solution quality and robustness in noisy search spaces. The thesis investigates the functioning and the characteristics of QiSNN using both synthetic feature selection benchmarks and a real-world case study on ecological modelling. By evolving suitable feature subsets, QiSNN significantly enhances the classification accuracy of eSNN. Compared to numerous other feature selection techniques, like the wrapper-based Multilayer Perceptron (MLP) and the Naive Bayesian Classifier (NBC), QiSNN demonstrates a competitive classification and feature selection performance while requiring comparatively low computational costs.
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Lei, Weidong. "Cyclic Hoist Scheduling Problems in Classical and Sustainabl." Thesis, Belfort-Montbéliard, 2014. http://www.theses.fr/2014BELF0244/document.

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Les ateliers de traitement de surface automatisés, qui utilisent des robots de manutention commandés par ordinateur pour le transport de la pièce, ont été largement mis en place dans différents types d'entreprises industrielles, en raison de ses nombreux avantages par rapport à un mode de production manuel, tels que : une plus grande productivité, une meilleure qualité des produits, et l’impact sur les rythmes de travail. Notre recherche porte sur trois types de problèmes d'ordonnancement associés à ces systèmes, appelés Hoist Scheduling Problems, caractérisés par des contraintes de fenêtres de temps de traitement: (I) un problème à une seule ressource de transport où l’objectif est de minimiser le temps de cycle; (II) un problème bi-objectif avec une seule ressource de transport où il faut minimiser le temps de cycle et la consommation de ressources de traitement (et par conséquent le coût de production); et (III) un problème d'ordonnancement cyclique mono-objectif mais multi-robots.En raison de la NP-complétude des problèmes étudiés et de nombreux avantages de les outils de type quantum-inspired evolutionary algorithm (QEA), nous proposons d'abord un QEA hybride comprenant un mécanisme de décodage amélioré et une procédure réparation dédiée pour trouver le meilleur temps de cycle pour le premier problème. Après cela, afin d'améliorer à la fois la performance économique et environnementale qui constituent deux des trois piliers de la stratégie de développement durable de nos jours déployée dans de nombreuses industries, nous formulons un modèle mathématique bi-objectif pour le deuxième problème en utilisant la méthode de l'intervalle interdit. Ensuite, nous proposons un QEA bi-objectif couplé avec une procédure de recherche locale pour minimiser simultanément le temps de cycle et les coûts de production, en générant un ensemble de solutions Pareto-optimales pour ce problème. Quant au troisième problème, nous constatons que la plupart des approches utilisées dans les recherches actuelles, telles que la programmation entière mixte (MIP), peuvent conduire à l’obtention d’une solution non optimale en raison de la prise en compte courante d’une hypothèse limitant l’exploration de l’espace de recherche et relative aux mouvements en charge des robots. Par conséquent, nous proposons une approche de MIP améliorée qui peut garantir l'optimalité des solutions obtenues pour ce problème, en relaxant l'hypothèse mentionnée ci-dessus.Pour chaque problème, une étude expérimentale a été menée sur des cas industriels ainsi que sur des instances générées aléatoirement. Les résultats obtenus montrent que l’efficacité des algorithmes d'ordonnancement proposés, ce qui justifie les choix que nous avons faits<br>Automated treatment surface facilities, which employ computer-controlled hoists for part transportation, have been extensively established in various kinds of industrial companies, because of its numerous advantages over manual system, such as higher productivity, better product quality, and reduced labor intensity. Our research investigates three typical hoist scheduling problems with processing time windows in treatment surface facilities, which are: (I) cyclic single-hoist scheduling problem to minimize the cycle time; (II) cyclic single-hoist scheduling problem to minimize the cycle time and processing resource consumption (and consequently production cost); and (III) cyclic multi-hoist scheduling problem to minimize the cycle time.Due to the NP-completeness of the studied problems and numerous advantages of quantum-inspired evolutionary algorithm (QEA), we first propose a hybrid QEA with improved decoding mechanism and repairing procedure to find the best cycle time for the first problem. After that, to enhance with both the economic and environmental performance, which constitute two of the three pillars of the sustainable strategy nowadays deployed in many industries, we formulate a bi-objective mathematical model for the second problem by using the method of prohibited interval. Then we propose a bi-objective QEA with local search procedure to simultaneously minimize the cycle time and production cost, and we find a set of Pareto-optimal solutions for this problem. As for the third problem, we find that most existing approaches, such as mixed integer programming (MIP) approach, may identify a non-optimal solution to be an optimal one due to an assumption related to the loaded hoist moves which is made in many existing researches. Consequently, we propose an improved MIP approach for this problem by relaxing the above-mentioned assumption. Our approach can guarantee the optimality of its obtained solutions.For each problem, experimental study on industrial instances and random instances has been conducted. Computational results demonstrate that the proposed scheduling algorithms are effective and justify the choices we made
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Zhang, G., J. X. Cheng, and Marian Gheorghe. "Dynamic Behavior Analysis of Membrane-Inspired Evolutionary Algorithms." 2014. http://hdl.handle.net/10454/10829.

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No<br>A membrane-inspired evolutionary algorithm (MIEA) is a successful instance of a model linking membrane computing and evolutionary algorithms. This paper proposes the analysis of dynamic behaviors of MIEAs by introducing a set of population diversity and convergence measures. This is the first attempt to obtain additional insights into the search capabilities of MIEAs. The analysis is performed on the MIEA, QEPS (a quantum-inspired evolutionary algorithm based on membrane computing), and its counterpart algorithm, QIEA (a quantum-inspired evolutionary algorithm), using a comparative approach in an experimental context to better understand their characteristics and performances. Also the relationship between these measures and fitness is analyzed by presenting a tendency correlation coefficient to evaluate the importance of various population and convergence measures, which is beneficial to further improvements of MIEAs. Results show that QEPS can achieve better balance between convergence and diversity than QIEA, which indicates QEPS has a stronger capacity of balancing exploration and exploitation than QIEA in order to prevent premature convergence that might occur. Experiments utilizing knapsack problems support the above made statement.
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Zhang, G., J. Chen, Marian Gheorghe, F. Ipate, and X. Wang. "QEAM: An Approximate Algorithm Using P Systems with Active Membranes." 2015. http://hdl.handle.net/10454/9256.

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No<br>This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compartments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark instances of satisfiability problem show that QEAM outperforms QEPS (quantum-inspired evolutionary algorithm based on P systems) and its counterpart quantum-inspired evolutionary algorithm.
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Liao, Yu-Hsun, and 廖宥勛. "A Quantum-inspired Evolutionary Clustering Algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/08720504651577151651.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>102<br>In recent years, a lot of evolutionary computation methods have been proposed to solve the combinatorial optimization problem based on the concepts of quantum mechanics. Although some of them are purposely presented for solving the data clustering problem, they are all far from optimal quality-wise. As such, this thesis proposes a new method, called quantum-inspired evolutionary clustering algorithm (QECA), to address the data clustering problem. The proposed method adds not only the concepts of clustering and the k-means to the traditional quantum-inspired evolutionary algorithm to make it work for clustering but also an effective repair operator to improve the performance of the proposed method. Experimental results on real world data show that the proposed method provides a promising result than those obtained by QEA and genetic k-means algorithm.
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CHEN, YAN-RONG, and 陳彥榮. "An Improved Quantum-Inspired Evolutionary Data Clustering Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/hm2q4n.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>106<br>Many metaheuristic algorithms have been presented to find an approximate solution for the data clustering problem in recent years. To understand the capability of quantum-based algorithm in solving the clustering problem and to improve the quality of the clustering results, an improved quantum-inspired evolutionary algorithm (iQEA) is presented in this thesis. Unlike the original QEA that fixes the rotation degree of Q-gate, the rotation degree of iQEA is changed at different iterations. The experimental results show that the iQEA is able to find a better result than the original QEA and all the other metaheuristic algorithms compared in this thesis in terms of the quality of the clustering results.
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Kuo, Shu-Yu, and 郭姝妤. "Entanglement Enhanced Quantum-inspired Search Algorithm for Solving Optimization Problem." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/j6n8he.

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博士<br>國立暨南國際大學<br>資訊工程學系<br>106<br>Solving optimization problem is an important issue in many areas. Many metaheuristic algorithms have been proposed to solve combinatorial and numerical optimization problems. Most optimization problems have dependency feature, meaning that variables are strongly dependent on one another. If a method were to attempt to optimize each variable independently, its performance would suffer significantly. When traditional optimization techniques are applied to high-dependency problems, they experience difficulty in finding the global optimum. To address this problem, this study proposes a novel metaheuristic algorithm, the entanglement-enhanced quantum-inspired tabu search algorithm (Entanglement-QTS), which is based on the quantum-inspired tabu search (QTS) algorithm and the feature of quantum entanglement. Entanglement-QTS differs from other quantum-inspired evolutionary algorithms in that its Q-bits have entangled states, which can express a high degree of correlation, rendering the variables more intertwined. Entangled Q-bits represent a state-of-the-art idea that can significantly improve the treatment of multimodal and high-dependency problems. Entanglement-QTS can discover optimal solutions, balance diversification and intensification, escape numerous local optimal solutions by using the quantum NOT gate, reinforce the intensification effect by local search and entanglement local search, and manage strong-dependency problems and accelerate the optimization process by using entangled states. This study uses nine benchmark functions to test the search ability of the Entanglement-QTS algorithm. The results demonstrate that Entanglement-QTS outperforms QTS and other metaheuristic algorithms in both its effectiveness at finding the global optimum and its computational efficiency. In addition, with recent advances in camera and visual computing technology, visual surveillance is playing a significant role in wireless sensor networks (WSNs) and cyber-physical systems (CPSs). It can be used in civilian areas for traffic control and security monitoring. Deployment is an important and fundamental issue in a WSN/CPS. Many issues, such as the quality of service, energy efficiency, and lifetime, are based on the placement of sensors. Different heuristic and deterministic methods have been proposed to achieve optimal deployment. In this study, Entanglement-QTS is applied to a deployment problem to determine the minimum number of sensors required and their locations. The experiment results showed that Entanglement-QTS outperformed other deployment approaches and used the least number of sensors to satisfy the monitoring requirement and topology connectivity. With Entanglement-QTS, the performance of surveillance system deployment has improved further.
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Books on the topic "Quantum inspired algorithms"

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Maulik, Ujjwal, Siddhartha Bhattacharyya, and Sandip Dey. Quantum Inspired Meta-Heuristics for Image Analysis. Wiley & Sons, Limited, John, 2019.

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Maulik, Ujjwal, Siddhartha Bhattacharyya, and Sandip Dey. Quantum Inspired Meta-Heuristics for Image Analysis. Wiley & Sons, Limited, John, 2019.

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Maulik, Ujjwal, Siddhartha Bhattacharyya, and Sandip Dey. Quantum Inspired Meta-Heuristics for Image Analysis. Wiley & Sons, Incorporated, John, 2019.

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Maulik, Ujjwal, Siddhartha Bhattacharyya, and Sandip Dey. Quantum Inspired Meta-Heuristics for Image Analysis. Wiley & Sons, Incorporated, John, 2019.

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Book chapters on the topic "Quantum inspired algorithms"

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Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Quantum Inspired Evolutionary Algorithms." In Natural Computing Algorithms. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_24.

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da Cruz, A. V. Abs, M. M. B. R. Vellasco, and M. A. C. Pacheco. "Quantum-Inspired Evolutionary Algorithm for Numerical Optimization." In Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_2.

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Wang, Ling, and Bin-bin Li. "Quantum-inspired genetic algorithms for flow shop scheduling." In Quantum Inspired Intelligent Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78532-3_2.

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Khullar, Vikas, Raj Gaurang Tiwari, and Ambuj Kumar Agarwal. "Quantum Layer-Inspired Deep Learning for Mechanical Parts Classification." In Algorithms for Intelligent Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7136-4_18.

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Fan, Kai, Anthony Brabazon, Conall O’Sullivan, and Michael O’Neill. "Quantum-Inspired Evolutionary Algorithms for Financial Data Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78761-7_14.

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Escovedo, Tatiana, Karla Figueiredo, Daniela Szwarcman, and Marley Vellasco. "Neuroevolutionary Models Based on Quantum-Inspired Evolutionary Algorithms." In Women in Computational Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79092-9_14.

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Boutekkouk, Fateh, and Soumia Oubadi. "Real Time Tasks Scheduling Optimization Using Quantum Inspired Genetic Algorithms." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33625-1_7.

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Wagner, Michael, Ludwig Kampel, and Dimitris E. Simos. "Quantum-Inspired Evolutionary Algorithms for Covering Arrays of Arbitrary Strength." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34029-2_20.

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Abs da Cruz, André V., Carlos R. Hall Barbosa, Marco Aurélio C. Pacheco, and Marley Vellasco. "Quantum-Inspired Evolutionary Algorithms and Its Application to Numerical Optimization Problems." In Neural Information Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_31.

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Lv, Fengmao, Guowu Yang, Shuangbao Wang, and Fuyou Fan. "The Research on Controlling the Iteration of Quantum-Inspired Evolutionary Algorithms for Artificial Neural Networks." In Algorithmic Aspects in Information and Management. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07956-1_23.

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Conference papers on the topic "Quantum inspired algorithms"

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Nowotniak, Robert, and Jacek Kucharski. "Higher-Order Quantum-Inspired Genetic Algorithms." In 2014 Federated Conference on Computer Science and Information Systems. IEEE, 2014. http://dx.doi.org/10.15439/2014f99.

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Chowdhury, S., S. Datta, and K. Y. Camsari. "A Probabilistic Approach to Quantum Inspired Algorithms." In 2019 IEEE International Electron Devices Meeting (IEDM). IEEE, 2019. http://dx.doi.org/10.1109/iedm19573.2019.8993655.

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Mohammed, Amgad M., N. A. Elhefnawy, Mahmoud M. El-Sherbiny, and Mohiy M. Hadhoud. "Quantum inspired evolutionary algorithms with parametric analysis." In 2014 Science and Information Conference (SAI). IEEE, 2014. http://dx.doi.org/10.1109/sai.2014.6918202.

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Zhang, Hua, Gexiang Zhang, Haina Rong, and Jixiang Cheng. "Comparisons of quantum rotation gates in quantum-inspired evolutionary algorithms." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5584179.

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da Cruz, Andre Vargas Abs, Marley M. B. R. Vellasco, and Marco Aurelio C. Pacheco. "Quantum-Inspired Evolutionary Algorithms applied to numerical optimization problems." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586193.

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Yetis, Hasan, and Mehmet Karakose. "Performance Comparison of Population-Based Quantum-Inspired Evolutionary Algorithms." In 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, 2019. http://dx.doi.org/10.1109/ubmyk48245.2019.8965624.

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Hongwen Liu, Gexiang Zhang, Chunxiu Liu, and Chun Fang. "A novel Memetic Algorithm based on real-observation Quantum-inspired evolutionary algorithms." In 2008 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE 2008). IEEE, 2008. http://dx.doi.org/10.1109/iske.2008.4730980.

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Silveira, Luciano R., Ricardo Tanscheit, and Marley Vellasco. "Quantum-inspired genetic algorithms applied to ordering combinatorial optimization problems." In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6256511.

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Yetis, Hasan, and Mehmet Karakose. "Collaborative Truck-Drone Routing Optimization Using Quantum-Inspired Genetic Algorithms." In 2021 25th International Conference on Information Technology (IT). IEEE, 2021. http://dx.doi.org/10.1109/it51528.2021.9390121.

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Yi, Shengqiu, Ming Chen, and Zhigao Zeng. "Convergence analysis on a class of quantum-inspired evolutionary algorithms." In 2011 Seventh International Conference on Natural Computation (ICNC). IEEE, 2011. http://dx.doi.org/10.1109/icnc.2011.6022161.

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