In this thesis, an evolutionary system for searching quantum operators in the form of unitary matrices is implemented. The aim is to propose several representations of candidate solutions and settings of the evolutionary algorithm. Two evolutionary algorithms were applied: the genetic algorithm and evolutionary strategy. Furthermore, a method of generating a unitary matrix is presented which is used for the first time for this task. This method is in some aspects better than the previous ones. Finally, a comparison of all used techniques is shown in experiments.

The work deals with the optimization of trusses construction building and transport machinery. The goal was to create an algorithm that can design an optimized design. The simulation took place on two experiments involving 52 sets of different entries, which are processed in detail into graphs. One-dimensional target mass or price function is used as part of optimization, but there is also an incorporated multidimensional purpose function. The finite element variation method for the beam system is used for the strength calculation of the truss structure and the genetic algorithm is used for optimization. At the end of the work, specific steps are formulated that lead to the most appropriate algorithm settings.

Kryptografie je velmi důležitým aspektem našeho každodenního života, protože poskytuje teoretický základ informační bezpečnosti. Kvantové výpočty a informace se také stávají velmi důležitou oblastí vědy kvůli mnoha aplikačním oblastem včetně kryptologie a konkrétněji v kryptografii veřejných klíčů. Obtížnost čísel do hlavních faktorů je základem některých důležitých veřejných kryptosystémů, jejichž klíčem je kryptosystém RSA . Shorův kvantový faktoringový al-goritmus využívá zejména kvantový interferenční účinek kvantového výpočtu k faktorovým semi-prime číslům v polynomiálním čase na kvantovém počítači. Ačkoli kapacita současných kvantových počítačů vykonávat Shorův algoritmus je velmi omezená, existuje mnoho rozsáhlých základních vědeckých výzkumů o různých technikách optimalizace algoritmu, pokud jde o faktory, jako je počet qubitů, hloubka obvodu a počet bran. v této práci jsou diskutovány, analyzovány a porovnávány různé varianty Shorova factoringového algoritmu a kvantových obvodů. Některé varianty Shorova algoritmu jsou také simulované a skutečně prováděné na simulátorech a kvantových počítačích na platformě IBM QuantumExperience. Výsledky simulace jsou porovnávány z hlediska jejich složitosti a míry úspěšnosti. Organizace práce je následující: Kapitola 1 pojednává o některých klíčových historických výsledcích kvantové kryptografie, uvádí problém diskutovaný v této práci a představuje cíle, kterých má být dosaženo. Kapitola 2 shrnuje matematické základy kvantového výpočtu a kryptografie veřejných klíčů a popisuje notaci použitou v celé práci. To také vysvětluje, jak lze k rozbití kryptosystému RSA použít realizovatelný algoritmus pro vyhledávání objednávek nebo factoring. Kapitola 3 představuje stavební kameny Shorova algoritmu, včetně kvantové Fourierovy transformace, kvantového odhadu fází, modulární exponentiace a Shorova algoritmu. Zde jsou také uvedeny a porovnány různé varianty optimalizace kvantových obvodů. Kapitola 4 představuje výsledky simulací různých verzí Shorova algoritmu. V kapitole 5 pojednejte o dosažení cílů disertační práce, shrňte výsledky výzkumu a nastíňte budoucí směry výzkumu.

Kvantová výpočetní technika je rychle rostoucí obor informatiky, který přenáší principy kvantových jevu do našeho každodenního života. Díky své kvantové podstatě získávají kvantové počítače převahu nad klasickými počítači. V této práci jsme se zaměřili na vysvětlení základů kvantového počítání a jeho implementaci na kvantovém počítači. Zejména se zaměřujeme na popis fungování, konstrukci a implementaci Groverova algoritmu jako jednoho ze základních kvantových algoritmů. Demonstrovali jsme sílu tohoto kvantového algoritmu při prohledávání databáze a porovnávali ho s klasickými nekvantovými algoritmy pomocí implementace prostřednictvím simulačního prostředí QISKit. Pro simulaci jsme použili QASM Simulator a State vector Simulator Aer backends a ukázali, že získané výsledky korelují s dříve diskutovanými teoretickými poznatky. Toto ukazuje, že Groverův algoritmus umožňuje kvadratické zrychlení oproti klasickému nekvantovému vyhledávacímu algoritmu, Použitelnost algoritmu stejně jako ostatních kvantových algoritmů je ale stále omezena několika faktory, mezi které patří vysoké úrovně dekoherence a chyby hradla.

An accurate estimate of the mass of a passenger vehicle is important for several safety systems and environmental aspects. In this thesis, an algorithm for estimating the mass of a passenger vehicle using the recursive least squares methodis presented. The algorithm is based on a physical model of the vehicle and is designed to be able to run in real-time onboard a vehicle and uses the wheel torque signal calculated in the electrical control unit in the engine. Therefore no estimation of the powertrain is needed. This is one contribution that distinguishes this thesis from previous work on the same topic, which has used the engine torque. The benefit of this is that no estimation of the dynamics in the powertrain is needed. The drawback of using this method is that the algorithm is dependenton the accuracy of the estimation done in the engine electrical control unit. Two different versions of the recursive least squares method (RLS) have been developed - one with a single forgetting factor and one with two forgetting factors. The estimation performance of the two versions are compared on several different real-world driving scenarios, which include driving on country roads, highways, and city roads, and different loads in the vehicle. The algorithm with a single forgetting factor estimates the mass with an average error for all tests of 4.42% and the algorithm with multiple forgetting factors estimates the mass with an average error of 4.15 %, which is in line with state-of-the-art algorithms that are presented in other studies. In a sensitivity analysis, it is shown that the algorithms are robust to changes in the drag coefficient. The single forgetting factor algorithm is robust to changes in the rolling resistance coefficient whereas the multiple forgetting factor algorithm needs the rolling resistance coefficient to be estimated with fairly good accuracy. Both versions of the algorithm need to know the wheel radius with an accuracy of 90 %. The results show that the algorithms estimate the mass accurately for all three different driving scenarios and estimate highway roads best with an average error of 2.83 % and 2.69 % for the single forgetting factor algorithm and the multiple forgetting factor algorithm, respectively. The results indicate it is possible to use either algorithm in a real-world scenario, where the choice of which algorithm depends on sought-after robustness.

<div>The design of advanced integrated circuits (ICs) and systems calls for multiphysics and large-scale modeling and simulation methods. On the one hand, novel devices and materials are emerging in next-generation IC technology, which requires multiphysics modeling and simulation. On the other hand, the ever-increasing complexity of ICs requires more efficient numerical solvers.</div><div><br></div><div>In this work, we propose a multiphysics modeling and simulation algorithm to co-simulate Maxwell's equations, dispersion relation of materials, and Boltzmann equation to characterize emerging new devices in IC technology such as Cu-Graphene (Cu-G) hybrid nano-interconnects. We also develop an unconditionally stable time marching scheme to remove the dependence of time step on space step for an efficient simulation of the multiscaled and multiphysics system. Extensive numerical experiments and comparisons with measurements have validated the accuracy and efficiency of the proposed algorithm. Compared to simplified steady-state-models based analysis, a significant difference is observed when the frequency is high or/and the dimension of the Cu-G structure is small, which necessitates our proposed multiphysics modeling and simulation for the design of advanced Cu-G interconnects. </div><div><br></div><div>To address the large-scale simulation challenge, we develop a new split-field domain-decomposition algorithm amenable for parallelization for solving Maxwell’s equations, which minimizes the communication between subdomains, while having a fast convergence of the global solution. Meanwhile, the algorithm is unconditionally stable in time domain. In this algorithm, unlike prevailing domain decomposition methods that treat the interface unknown as a whole and let it be shared across subdomains, we partition the interface unknown into multiple components, and solve each of them from one subdomain. In this way, we transform the original coupled system to fully decoupled subsystems to solve. Only one addition (communication) of the interface unknown needs to be performed after the computation in each subdomain is finished at each time step. More importantly, the algorithm has a fast convergence and permits the use of a large time step irrespective of space step. Numerical experiments on large-scale on-chip and package layout analysis have demonstrated the capability of the new domain decomposition algorithm. </div><div><br></div><div>To tackle the challenge of efficient simulation of irregular structures, in the last part of the thesis, we develop a method for the stability analysis of unsymmetrical numerical systems in time domain. An unsymmetrical system is traditionally avoided in numerical formulation since a traditional explicit simulation is absolutely unstable, and how to control the stability is unknown. However, an unsymmetrical system is frequently encountered in modeling and simulating of unstructured meshes and nonreciprocal electromagnetic and circuit devices. In our method, we reduce stability analysis of a large system into the analysis of dissembled single element, therefore provides a feasible way to control the stability of large-scale systems regardless of whether the system is symmetrical or unsymmetrical. We then apply the proposed method to prove and control the stability of an unsymmetrical matrix-free method that solves Maxwell’s equations in general unstructured meshes while not requiring a matrix solution.<br></div><div><br></div>

L’algorithme EM (Dempster et al., 1977) permet de construire une séquence d’estimateurs qui converge vers l’estimateur de vraisemblance maximale pour des modèles à données manquantes pour lesquels l’estimateur du maximum de vraisemblance n’est pas calculable. Cet algorithme est remarquable compte tenu de ses nombreuses applications en apprentissage statistique. Toutefois, il peut avoir un lourd coût computationnel. Les auteurs Cappé et Moulines (2009) ont proposé une version en ligne de cet algorithme pour les modèles appartenant à la famille exponentielle qui permet de faire des gains d’efficacité computationnelle importants en présence de grands jeux de données. Cependant, le calcul de l’espérance a posteriori de la statistique exhaustive, qui est nécessaire dans la version de Cappé et Moulines (2009), est rarement possible pour des modèles complexes et/ou lorsque la dimension des données manquantes est grande. On doit alors la remplacer par un estimateur. Plusieurs questions se présentent naturellement : les résultats de convergence de l’algorithme initial restent-ils valides lorsqu’on remplace l’espérance par un estimateur ? En particulier, que dire de la normalité asymptotique de la séquence des estimateurs ainsi créés, de la variance asymptotique et de la vitesse de convergence ? Comment la variance de l’estimateur de l’espérance se reflète-t-elle sur la variance asymptotique de l’estimateur EM? Peut-on travailler avec des estimateurs de type Monte-Carlo ou MCMC? Peut-on emprunter des outils populaires de réduction de variance comme les variables de contrôle ? Ces questions seront étudiées à l’aide d’exemples de modèles à variables latentes. Les contributions principales de ce mémoire sont une présentation unifiée des algorithmes EM d’approximation stochastique, une illustration de l’impact au niveau de la variance lorsque l’espérance a posteriori est estimée dans les algorithmes EM en ligne et l’introduction d’algorithmes EM en ligne permettant de réduire la variance supplémentaire occasionnée par l’estimation de l’espérance a posteriori. / The EM algorithm Dempster et al. (1977) yields a sequence of estimators that converges to the maximum likelihood estimator for missing data models whose maximum likelihood estimator is not directly tractable. The EM algorithm is remarkable given its numerous applications in statistical learning. However, it may suffer from its computational cost. Cappé and Moulines (2009) proposed an online version of the algorithm in models whose likelihood belongs to the exponential family that provides an upgrade in computational efficiency in large data sets. However, the conditional expected value of the sufficient statistic is often intractable for complex models and/or when the missing data is of a high dimension. In those cases, it is replaced by an estimator. Many questions then arise naturally: do the convergence results pertaining to the initial estimator hold when the expected value is substituted by an estimator? In particular, does the asymptotic normality property remain in this case? How does the variance of the estimator of the expected value affect the asymptotic variance of the EM estimator? Are Monte-Carlo and MCMC estimators suitable in this situation? Could variance reduction tools such as control variates provide variance relief? These questions will be tackled by the means of examples containing latent data models. This master’s thesis’ main contributions are the presentation of a unified framework for stochastic approximation EM algorithms, an illustration of the impact that the estimation of the conditional expected value has on the variance and the introduction of online EM algorithms which reduce the additional variance stemming from the estimation of the conditional expected value.

Diploma thesis deals with usage of artificial coordinate systems used for localization of a station on the internet and prediction of delay between the stations. There are described and compared basic properties of centralized and decentralized algorithms providing station localization on the internet and RTT prediction. More in depth are presented main representatives of both types of algorithms such as GNP, IDMAPS or Lighthouse. Central part of thesis is aimed at getting to know Vivaldi distributed algorithm. Basic principle of the algorithm for constant and variable time step, using two dimensional coordinate system with 3rd parameter height, is here outlined. Further more implementation of this algorithm as a library Vivaldi-lib in the environment of Java is implemented. Part of the thesis are simulations of behaviour of this algorithm for both variations realized on artificial networks and data obtained from PlanetLab experimental network, using simulation created program VIVALDIMONITOR.

This master thesis deals with the automatic design of examinations and courses scheduling. The design is adapted to the specific requirements of the Faculty of Information Technology of Brno University of Technology. A genetic algorithm and a heuristic algorithm are employed to solve this task. The genetic algorithm is used to specify the sequence of the examinations (or the courses) and then the heuristic algorithm spread them out into a timetable. An implementation (written in Python 3) provides a fast parallel processing calculation which can generate satisfactory schedules in tens of minutes. Performed experiments show approximately 13% better results in all considered criteria in comparison with utilized examination schedules in the past. The development was periodically consulted with persons responsible for the schedule processing at the faculty. The program will be used while designing of examination schedules for the academic year 2015/2016.

The thesis focuses on a new concept of cellular automata control based on instructions. The instruction can be understood as a rule that checks the states of cells in pre-defined areas in the cellular neighbourhood. If a given condition is satisfied, the state of the central cell is changed according to the definition of the instruction. Because it's possible to perform more instructions in one computational step, their sequence can be understood as a form of a short program. This concept can be extended with simple operations applied to the instruction's prescription during interpretation of the instructions - an example of such operation can be row shift or column shift. An advantage of the instruction-based approach lies in the search space reduction. In comparison with the table-based approach, it isn't necessary to search all the possible configurations of the cellular neighbouhood, but only several areas determined by the instructions. While the groups of the inspected cells in the cellular neighbourhood are designed manually on the basis of the analysis of the solved task, their sequence in the chromosome is optimized by genetic algorithm. The capability of the proposed method of cellular automata control is studied on these benchmark tasks - majority, synchronization, self-organization and the design of combinational circuits.