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Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Dikme, Altay. "A Quantum Neural Network for Noisy Intermediate Scale Quantum Devices." Thesis, KTH, Tillämpad fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300394.

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Neural networks have helped the field of machine learning grow tremendously in the past decade, and can be used to solve a variety of real world problems such as classification problems. On another front, the field of quantum computing has advanced, with quantum devices publicly available via the cloud. The availability of such systems has led to the creation of a new field of study, Quantum Machine Learning, which attempts to create quantum analogues of classical machine learning techniques. One such method is the Quantum Neural Network (QNN) inspired by classical neural networks. In this the
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SILVA, Adenilton José da. "Artificial neural network architecture selection in a quantum computer." UNIVERSIDADE FEDERAL DE PERNAMBUCO, 2015. https://repositorio.ufpe.br/handle/123456789/15011.

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Submitted by Isaac Francisco de Souza Dias (isaac.souzadias@ufpe.br) on 2016-01-27T17:25:47Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese Adenilton José da Silva.pdf: 4885126 bytes, checksum: d2bade12d15d6626962f244aebd5678d (MD5)<br>Made available in DSpace on 2016-01-27T17:25:47Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese Adenilton José da Silva.pdf: 4885126 bytes, checksum: d2bade12d15d6626962f244aebd5678d (MD5) Previous issue date: 2015-06-26<br>CNPq<br>Miniaturisa
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Gandhi, Vaibhav Sudhir. "Quantum neural network based EEG filtering and adaptive brain-robot interfaces." Thesis, Ulster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573092.

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Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. Dealing with the unknown embedded noise within the raw EEG and the inherent lower bandwidth of BCI are still two of the major challenges in making BC! practical for day-to-day use. The raw EEG signal recorded non-invasively during motor i ~~~y (MI) is intrinsically " ." embedded with non-Gaussian noise while the actual noise-free EEG has so far not been attained. The
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Mori, Yuto. "Path optimization with neural network for sign problem in quantum field theories." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263466.

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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 dramaticall
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Czischek, Stefanie [Verfasser], and Thomas [Akademischer Betreuer] Gasenzer. "Simulating Strongly Interacting Quantum Spin Systems–From Critical Dynamics Towards Entanglement Correlations in a Classical Artificial Neural Network / Stefanie Czischek ; Betreuer: Thomas Gasenzer." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://d-nb.info/119790431X/34.

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Alzate, Banguero Melissa. "Towards neuromorphic computing on quantum many-body architectures : VO2 transition dynamics." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS021.

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Alors que les exigences en matière d'IA augmentent, de nouveaux paradigmes informatiques deviennent essentiels. Les architectures traditionnelles de von Neumann peinent à répondre aux exigences intensives de l'IA. L'informatique neuromorphique, inspirée par le cerveau, intègre traitement et mémoire pour une computation plus rapide et efficace, idéale pour des applications d'IA comme l'apprentissage profond et la reconnaissance de formes. Les matériaux clés pour l'informatique neuromorphique incluent les synaptors et les neuristors. Les memristors, des mémoires non volatiles fabriquées à partir
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Cîrstea, Bogdan-Ionut. "Contribution à la reconnaissance de l'écriture manuscrite en utilisant des réseaux de neurones profonds et le calcul quantique." Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0059.

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Dans cette thèse, nous fournissons plusieurs contributions des domaines de l’apprentissage profond et du calcul quantique à la reconnaissance de l’écriture manuscrite. Nous commençons par intégrer certaines des techniques d’apprentissage profond les plus récentes(comme dropout, batch normalization et différentes fonctions d’activation) dans les réseaux de neurones à convolution et obtenons des meilleures performances sur le fameux jeu de données MNIST. Nous proposons ensuite des réseaux TSTN (Tied Spatial Transformer Networks), une variante des réseaux STN (Spatial Transformer Networks) avec p
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Hutin, Hector. "Mesure et contrôle assisté par réseau de neurones de modes micro-ondes quantiques avec des circuits supraconducteurs." Electronic Thesis or Diss., Lyon, École normale supérieure, 2024. http://www.theses.fr/2024ENSL0092.

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L'association d'un bit quantique et d'un mode harmonique couplé dispersivement est l'un des systèmes les plus étudiés dans le domaine de l'électrodynamique quantique de circuits supraconducteurs. Il offre un des moyens actuels les plus fiables pour mesurer l'état d'un bit quantique de manière non-destructive, et a permis nombre d'expériences d'information quantique étudiant l'information obtenue via cette mesure et la dynamique du qubit lorsqu'il y est soumis. Il offre également la possibilité de préparer et de manipuler l'état quantique du mode harmonique grâce au qubit, ce qui permet de stoc
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Wu, Jiaxin. "Topics in Cold Atoms Related to Quantum Information Processing and A Machine Learning Approach to Condensed Matter Physics." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156320039156199.

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Menneer, Tamaryn Stable Ia. "Quantum artificial neural networks." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286530.

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Ahamed, Woakil Uddin. "Quantum recurrent neural networks for filtering." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:2411.

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The essence of stochastic filtering is to compute the time-varying probability densityfunction (pdf) for the measurements of the observed system. In this thesis, a filter isdesigned based on the principles of quantum mechanics where the schrodinger waveequation (SWE) plays the key part. This equation is transformed to fit into the neuralnetwork architecture. Each neuron in the network mediates a spatio-temporal field witha unified quantum activation function that aggregates the pdf information of theobserved signals. The activation function is the result of the solution of the SWE. Theincorpor
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13

Pesah, Arthur. "Learning quantum state properties with quantum and classical neural networks." Thesis, KTH, Tillämpad fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252693.

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14

Österberg, Viktor. "Using Machine Learning to Develop a Quantum-Accurate Inter-Atomic Potential for Large Scale Molecular Dynamics Simulations of Iron under Earth’s Core Conditions." Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298848.

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Measurements of iron at extreme pressures do not agree on the melting temperature for conditions comparable with those believed to hold at Earth's core. To attempt to determine the stability of relevant lattices, simulations involving a huge amount of particles are needed. In this thesis, a machine learned model is trained to yield results from density functional theory. Different machine learning models are compared. The trained model is then used in molecular dynamics simulations of relevant lattices at a scale too large for density functional theory.<br>Mätningar av järns smälttemperatur un
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Du, Yuxuan. "The Power of Quantum Neural Networks in The Noisy Intermediate-Scale Quantum Era." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24976.

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Machine learning (ML) has revolutionized the world in recent years. Despite the success, the huge computational overhead required by ML models makes them approach the limits of Moore’s law. Quantum machine learning (QML) is a promising way to conquer this issue, empowered by Google's demonstration of quantum computational supremacy. Meanwhile, another cornerstone in QML is validating that quantum neural networks (QNNs) implemented on the noisy intermediate-scale quantum (NISQ) chips can accomplish classification and image generation tasks. Despite the experimental progress, little is known abo
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16

Linn, Hanna. "Detecting quantum speedup for random walks with artificial neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289347.

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Random walks on graphs are an essential base for crucial algorithms for solving problems, like the boolean satisfiability problem. A speedup of random walks could improve these algorithms. The quantum version of the random walk, quantum walk, is faster than random walks in specific cases, e.g., on some linear graphs. An analysis of when the quantum walk is faster than the random walk can be accomplished analytically or by simulating both the walks on the graph. The problem arises when the graphs grow in size and connectivity. There are no known general rules for what an arbitrary graph not hav
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Hu, Zhizhai, University of Western Sydney, of Science Technology and Environment College, and School of Computing and Information Technology. "Quantum computation via neural networks applied to image processing and pattern recognition." THESIS_CSTE_CIT_Hu_Z.xml, 2001. http://handle.uws.edu.au:8081/1959.7/136.

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This thesis explores moving information processing by means of quantum computation technology via neural networks. A new quantum computation algorithm achieves a double-accurate outcome on measuring optical flows in a video. A set of neural networks act as experimental tools that manipulate the applied data. Attempts have been made to calculate a pixel's location, velocity and grey scale value of moving images but the location and velocity could not be simultaneously measured precisely enough in accordance with both classical and quantum uncertainty principles. The error in measurement produce
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Hu, Zhizhai. "Quantum computation via neural networks applied to image processing and pattern recognition." Thesis, View thesis, 2001. http://handle.uws.edu.au:8081/1959.7/136.

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This thesis explores moving information processing by means of quantum computation technology via neural networks. A new quantum computation algorithm achieves a double-accurate outcome on measuring optical flows in a video. A set of neural networks act as experimental tools that manipulate the applied data. Attempts have been made to calculate a pixel's location, velocity and grey scale value of moving images but the location and velocity could not be simultaneously measured precisely enough in accordance with both classical and quantum uncertainty principles. The error in measurement produce
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Hu, Zhizhai. "Quantum computation via neural networks applied to image processing and pattern recognition /." View thesis, 2001. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030731.105740/index.html.

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Thesis (PhD) -- University of Western Sdyney, 2001.<br>"A thesis submitted for the Degree of Doctor of Philosophy of the University of Western Sydney, Australia. School of Computing and Information Technology." "August 2001" Bibliography: leaves 136 - 143.
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Ellis, Mathys. "Regularised feed forward neural networks for streamed data classification problems." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/75804.

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Streamed data classification problems (SDCPs) require classifiers with the ability to learn and to adjust to the underlying relationships in data streams, in real-time. This requirement poses a challenge to classifiers, because the learning task is no longer just to find the optimal decision boundaries, but also to track changes in the decision boundaries as new training data is received. The challenge is due to concept drift, i.e. the changing of decision boundaries over time. Changes include disappearing, appearing, or shifting decision boundaries. This thesis proposes an online learning appro
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21

Semerjian, Guilhem. "Mean-field disordered systems : glasses and optimization problems, classical and quantum." Habilitation à diriger des recherches, Ecole Normale Supérieure de Paris - ENS Paris, 2013. http://tel.archives-ouvertes.fr/tel-00785924.

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Ce mémoire présente mes activités de recherche dans le domaine de la mécanique statistique des systèmes désordonnés, en particulier sur les modèles de champ moyen à connectivité finie. Ces modèles présentent de nombreuses transitions de phase dans la limite thermodynamique, avec des applications tant pour la physique des verres que pour leurs liens avec des problèmes d'optimisation de l'informatique théorique. Leur comportement sous l'effet de fluctuations quantiques est aussi discuté, en lien avec des perspectives de calcul quantique.
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Jaffali, Hamza. "Étude de l'Intrication dans les Algorithmes Quantiques : Approche Géométrique et Outils Dérivés." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA017.

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L’intrication quantique est un des phénomènes les plus intéressants et intriguant en Mécanique Quantique, et de surcroît en Théorie de l’Information Quantique. Ressource fondamentale pour le calcul quantique, son rôle dans l’efficacité et la fiabilité des protocoles ou algorithmes quantiques n’est toujours pas totalement compris. Dans cette thèse, nous étudions l’intrication quantique des états multipartites, et notamment la nature de sa présence dans les algorithmes quantiques. L’étude de l’intrication se fait d’un point de vue théorique, en utilisant principalement des outils issus de la géo
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Benigni, Lucas. "Dynamics of eigenvectors of random matrices and eigenvalues of nonlinear models of matrices." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC003/document.

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Cette thèse est constituée de deux parties indépendantes. La première partie concerne l'étude des vecteurs propres de matrices aléatoires de type Wigner. Dans un premier temps, nous étudions la distribution des vecteurs propres de matrices de Wigner déformées, elles consistent en une perturbation d'une matrice de Wigner par une matrice diagonale déterministe. Si les deux matrices sont du même ordre de grandeur, il a été prouvé que les vecteurs propres se délocalisent complètement et les valeurs propres rentrent dans la classe d'universalité de Wigner-Dyson-Mehta. Nous étudions ici une phase in
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Rodrigues, Pedro João. "Técnicas de segmentação e de classificação em imagens. Estudo de um caso de aplicação." Doctoral thesis, Universidade do Minho, 2008. http://hdl.handle.net/10198/1499.

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Os processos de segmentação de imagens existentes continuam a estar intensamente relacionados com o problema para o qual foram pensados. Um sistema que produz segmentações de elevado sucesso num determinado problema pode falhar por completo se aplicado, sem modificações, a um outro problema. Mecanismos de segmentação que se aproximem, em capacidade generalista, ao sistema visual humano têm sido procurados pela comunidade científica. Nesta tese são estudadas técnicas e apresentados alguns contributos orientados para a melhoria da capacidade generalista e do desempenho dos sistemas de segmentaçã
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Burdin, Sébastien. "Théories de champ moyen pour les systèmes d'électrons à fortes corrélations." Habilitation à diriger des recherches, Université Sciences et Technologies - Bordeaux I, 2012. http://tel.archives-ouvertes.fr/tel-00711167.

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Ce mémoire d'habilitation à diriger les recherches présente des théories de champ moyen que j'ai appliquées à l'étude de systèmes d'électrons à fortes corrélations. Il s'appuie sur des travaux que j'ai effectués, pour certains dans la continuité de ma thèse, pour d'autres dans des directions nouvelles. L'une des problématiques centrales est celle des transitions de phases quantiques, et les systèmes considérés ont le point commun de décrire des impuretés quantiques, réelles ou artificielles. Les différentes méthodes de champ moyen utilisées sont présentées par des exemples dans le cadre de pro
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Vermersch, Benoît. "Dynamique d'un gaz de bosons ultra-froids dans un milieu désordonné : Effets des interactions sur la localisation et sur la transition d'Anderson." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2013. http://tel.archives-ouvertes.fr/tel-00910405.

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En présence de désordre, la diffusion des particules peut être complètement annihilée, don- nant lieu à la fameuse localisation d'Anderson. En dimension trois, une transition de phase sépare une telle phase isolante du régime diffusif. À partir de différentes approches théo- riques et numériques, cette thèse a pour objectif de déterminer l'effet des interactions entre particules sur la localisation d'Anderson et sur la transition d'Anderson, dans le contexte expérimental des condensats de Bose-Einstein. Dans le cas unidimensionnel, la compétition entre désordre et interaction
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Lee, Chang-Teh, and 李昌德. "Power Load Forecasting by Using Quantum Neural Network." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/69606706693514989599.

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碩士<br>義守大學<br>電機工程學系<br>92<br>In this thesis, the non-stationary signal prediction by using quantum neural network (QNN) is proposed. The signals with fuzziness are expected to be classified clearly for enhancing the learning efficiency of neural network due to the hidden units with various graded levels in QNN structure. As we know, power load forecasting is a type of non-stationary signal processing. Such an environment of signal information is generally complex and ill defined; also the behavior of power load is time-varying and dynamic. It sometimes might make NN have an ill lea
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Tsai, Xin-Yi, and 蔡信一. "Non-Convex Systems Identification by Using Quantum Neural Network." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/32393895701743519531.

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碩士<br>義守大學<br>電機工程學系<br>92<br>In this thesis, the non-convex system identification by using quantum neural network (QNN) is studied and simulated. The signals with fuzziness and uncertainties are expected can be effectively identified since the structure of hidden units with various graded levels. As we know, NN has been popularly applied in areas such as pattern recognition, signal processing, system identification and so on due to its powerful nonlinear mapping property and its ability to learn from training examples. Usually, the NN classifier can sort a set of feature patterns i
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Chen, Cheng-Hung, and 陳政宏. "A Self-Organizing Quantum Neural Fuzzy Network for Classification Applications." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/4h2vbk.

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碩士<br>朝陽科技大學<br>資訊工程系碩士班<br>92<br>In this thesis, a quantum neural fuzzy network (QNFN) for classification applications is proposed. The QNFN model is a four-layer structure. Layer 2 of the QNFN model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-organizing learning algorithm, which consists of the self-clustering algorithm (SCA) and the backpropagation algorithm, is also proposed. The proposed the SCA method is a fast, one-pass algorithm for a d
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Chiu, Shih-Chi, and 邱士齊. "Hybrid Quantum Genetic Algorithm and Artificial Neural Network for Short Term Load Forecasting." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/8538t3.

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碩士<br>國立臺北科技大學<br>電機工程系研究所<br>100<br>Power load forecasting is one of the essential factors of electric utilities when planning the future electricity supplies. Precise forecasting of electricity consumption may not only provide proper generation commitment and scheduling but also reduce the operational cost. It can also maintain the reliability of power systems, avoiding electricity crisis or wasting resources. It can further increase the power quality of power systems and the competitiveness of utilities. This thesis presents a hybrid Quantum Genetic Algorithm (QGA) and Back-Propagation
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Aulady, Mohamad, and 馬天王. "A Hybrid Symbiotic Organisms Search-Quantum Neural Network for Predicting High Performance Concrete Compressive Strength." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/74038213400285365840.

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碩士<br>國立臺灣科技大學<br>營建工程系<br>102<br>This paper presents a new hybrid technique called Symbiotic Organism Search-Quantum Neural Network (SOS-QNN) to predict the compressive strength of high performance Concrete. QNN refers to the class of neural network models, artificial or biological, which rely on principles inspired in some way from quantum mechanics. While SOS is a new optimization technique. The performance of SOS-QNN is compared with artificial neural network (ANN) and quantum neural network (QNN) itself. SOS function is to optimize all parameters within QNN so it will improve QNN ability
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Leelar, Bhawani Shankar. "Machine Learning Algorithms Using Classical And Quantum Photonics." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4303.

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ABSTRACT In the modern day , we are witnessing two complementary trends, exponential growth in data and shrinking of chip size. The Data is approaching to 44 zettabytes by 2020 and the chips are now available with 10nm technology. The hyperconnectivity between machine-to-machine and humanto- machine creates multi-dimensional data which is more complex. Our thesis addresses the quantum meta layer abstraction which provides the interface to the Application layer to design quantum and classical algorithms. The first part of the thesis addresses the quantum algorithms and second part address cl
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Slatyer, Harry James. "Multi-parameter optimisation of quantum optical systems." Phd thesis, 2018. http://hdl.handle.net/1885/146120.

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Quantum optical systems are poised to become integral components of technologies of the future. While there is growing commercial interest in these systems---for applications in information processing, secure communication and precision metrology---there remain significant technical challenges to overcome before widespread adoption is possible. In this thesis we consider the general problem of optimising quantum optical systems, with a focus on sensing and information processing applications. We investigate four different classes of system with varying d
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Schmitt, Markus. "Dynamics of isolated quantum many-body systems far from equilibrium." Doctoral thesis, 2018. http://hdl.handle.net/11858/00-1735-0000-002E-E32A-F.

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Terrett, Andrew J. "Neural networks for legal quantum prediction." Thesis, 1994. http://hdl.handle.net/2429/3460.

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This thesis argues that Artificial Neural Networks (ANN's) have applications within the domain of law and can be built using readily available Artificial Intelligence software for the Personal Computer. In order to demonstrate this, I have built working ANN's using data made available to me by the Faculty of Law Artificial Intelligence Research Project (FLAIR) and Windows™-based ANN software that is commercially available. The reasons for building a system are three-fold. First, a working system is the most graphic demonstration of my above assertion. Secondly, given my background as a p
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Liu, Sheng-Wu, and 劉生武. "Fuzzy Based Quantum Neural Networks for Short-term Load Forecasting." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/88117540993888426283.

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碩士<br>中原大學<br>電機工程研究所<br>94<br>This thesis presents a fuzzy based quantum neural networks method for short-term load forecasting. By using the nature of the quantum neural network. the relationship between the load and the influencing factors can be derived through self-learning to establish a forecasting model for non-stationary power load signals. With larger the learning rate , we have larger the correction values of the interconnecting weights to speed up the learning. However, it may also cause excessive correction and lead to oscillation and divergence. With a smaller learning rate, the
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楊振雄. "Hyperchaos, Chaos Control, Chaotization and Synchronization for a Quantum Cellular Neural Networks Nano System." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/85549424335994773961.

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博士<br>國立交通大學<br>機械工程系所<br>96<br>Abstract Chaos exists everywhere in the universe, from interstellar space to interatomic interval. In this thesis, coupled quantum-dot cells will be studied. They are used for Quantum-dots Cellular Automata (QCA), to build Cellular Neural Networks (CNN). It is shown that the connection of even only two quantum-dot cells can cause the onset of chaotic motion by small difference of polarizations and template between cells. This study could be very important for future ultra-small realization of CNNs and, on the other hand, for performing quantum computation effici
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Hardy, Yorick. "Classical and quantum computing." Thesis, 2008. http://hdl.handle.net/10210/489.

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Rodrigues, Pedro João Soares. "Técnicas de segmentação e de classificação em imagens: estudo de um caso de aplicação." Doctoral thesis, 2008. http://hdl.handle.net/1822/8909.

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Tese de Doutoramento Área de Informática Industrial - Ramo do Conhecimento Electrónica Industrial<br>Os processos de segmentação de imagens existentes continuam a estar intensamente relacionados com o problema para o qual foram pensados. Um sistema que produz segmentações de elevado sucesso num determinado problema pode falhar por completo se aplicado, sem modificações, a um outro problema. Mecanismos de segmentação que se aproximem, em capacidade generalista, ao sistema visual humano têm sido procurados pela comunidade científica. Nesta tese são estudadas técnicas e apresentados alguns
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