Relevant bibliographies by topics / Algorithmie quantique

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Algorithmie quantique'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Algorithmie quantique"

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Pavel, Ilarion. "Les défis des technologies quantiques." Annales des Mines - Responsabilité et environnement N° 114, no. 2 (2024): 81–90. http://dx.doi.org/10.3917/re1.114.0081.

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Le développement des technologies quantiques est aujourd’hui l’objet d’importants efforts de recherche, mais les résultats seront-ils à la hauteur des attentes ? L’ordinateur quantique peut résoudre certains problèmes difficiles, qui demandent à l’ordinateur classique un temps de calcul trop important, et peut également attaquer les schémas actuels de cryptage. Cependant, la réalisation d’un ordinateur quantique suffisamment puissant pour résoudre des problèmes pratiques demeure un véritable défi technologique. Il existe plusieurs technologies d’implémentation hardware , chacune avec ses avantages et ses inconvénients. Comme ils sont plus faciles à réaliser, la recherche se dirige également vers la mise au point d’ordinateurs quantiques analogiques et de simulateurs quantiques. Parallèlement, ces travaux ont pour conséquence la conception de capteurs extrêmement sensibles, qui ont de nombreuses applications dans l’industrie de la prospection géologique, dans l’imagerie médicale, dans les technologies militaires et la mise au point de nouvelles méthodes de cryptage immunes contre l’attaque par un algorithme quantique.
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Blais, A. "Algorithmes et architectures pour ordinateurs quantiques supraconducteurs." Annales de Physique 28, no. 5 (2003): 1–148. http://dx.doi.org/10.1051/anphys:2003008.

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Rahman, Mohammad Arshad. "Quantile regression using metaheuristic algorithms." International Journal of Computational Economics and Econometrics 3, no. 3/4 (2013): 205. http://dx.doi.org/10.1504/ijcee.2013.058498.

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MOUNT, DAVID M., NATHAN S. NETANYAHU, CHRISTINE D. PIATKO, RUTH SILVERMAN, and ANGELA Y. WU. "QUANTILE APPROXIMATION FOR ROBUST STATISTICAL ESTIMATION AND k-ENCLOSING PROBLEMS." International Journal of Computational Geometry & Applications 10, no. 06 (2000): 593–608. http://dx.doi.org/10.1142/s0218195900000334.

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Given a set P of n points in Rd, a fundamental problem in computational geometry is concerned with finding the smallest shape of some type that encloses all the points of P. Well-known instances of this problem include finding the smallest enclosing box, minimum volume ball, and minimum volume annulus. In this paper we consider the following variant: Given a set of n points in Rd, find the smallest shape in question that contains at least k points or a certain quantile of the data. This type of problem is known as a k-enclosing problem. We present a simple algorithmic framework for computing quantile approximations for the minimum strip, ellipsoid, and annulus containing a given quantile of the points. The algorithms run in O(n log n) time.
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Kibzun, A. I. "Parallelization of the quantile function optimization algorithms." Automation and Remote Control 68, no. 5 (2007): 799–810. http://dx.doi.org/10.1134/s0005117907050074.

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Papacharalampous, Georgia, Hristos Tyralis, Andreas Langousis, et al. "Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms." Water 11, no. 10 (2019): 2126. http://dx.doi.org/10.3390/w11102126.

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We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catchments over the contiguous United States. Point hydrological predictions are obtained using the Génie Rural à 4 paramètres Journalier (GR4J) hydrological model and exploited as predictor variables within quantile regression settings. Six machine-learning quantile regression algorithms and their equal-weight combiner are applied to predict conditional quantiles of the hydrological model errors. The individual algorithms are quantile regression, generalized random forests for quantile regression, generalized random forests for quantile regression emulating quantile regression forests, gradient boosting machine, model-based boosting with linear models as base learners and quantile regression neural networks. The conditional quantiles of the hydrological model errors are transformed to conditional quantiles of daily streamflow, which are finally assessed using proper performance scores and benchmarking. The assessment concerns various levels of predictive quantiles and central prediction intervals, while it is made both independently of the flow magnitude and conditional upon this magnitude. Key aspects of the developed methodological framework are highlighted, and practical recommendations are formulated. In technical hydro-meteorological applications, the algorithms should be applied preferably in a way that maximizes the benefits and reduces the risks from their use. This can be achieved by (i) combining algorithms (e.g., by averaging their predictions) and (ii) integrating algorithms within systematic frameworks (i.e., by using the algorithms according to their identified skills), as our large-scale results point out.
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Zheng, Songfeng. "Gradient descent algorithms for quantile regression with smooth approximation." International Journal of Machine Learning and Cybernetics 2, no. 3 (2011): 191–207. http://dx.doi.org/10.1007/s13042-011-0031-2.

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Möller, Eva, Gert Grieszbach, Bärbel Schack, and Herbert Witte. "Statistical Properties and Control Algorithms of Recursive Quantile Estimators." Biometrical Journal 42, no. 6 (2000): 729–46. http://dx.doi.org/10.1002/1521-4036(200010)42:6<729::aid-bimj729>3.0.co;2-w.

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Xiang, Dao-Hong, Ting Hu, and Ding-Xuan Zhou. "Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression." Journal of Applied Mathematics 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/902139.

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We study learning algorithms generated by regularization schemes in reproducing kernel Hilbert spaces associated with anϵ-insensitive pinball loss. This loss function is motivated by theϵ-insensitive loss for support vector regression and the pinball loss for quantile regression. Approximation analysis is conducted for these algorithms by means of a variance-expectation bound when a noise condition is satisfied for the underlying probability measure. The rates are explicitly derived under a priori conditions on approximation and capacity of the reproducing kernel Hilbert space. As an application, we get approximation orders for the support vector regression and the quantile regularized regression.
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Cheng, Hao. "Comparison of partial least square algorithms in hierarchical latent variable model with missing data." SIMULATION 96, no. 10 (2020): 825–39. http://dx.doi.org/10.1177/0037549720944467.

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Missing data is almost inevitable for various reasons in many applications. For hierarchical latent variable models, there usually exist two kinds of missing data problems. One is manifest variables with incomplete observations, the other is latent variables which cannot be observed directly. Missing data in manifest variables can be handled by different methods. For latent variables, there exist several kinds of partial least square (PLS) algorithms which have been widely used to estimate the value of latent variables. In this paper, we not only combine traditional linear regression type PLS algorithms with missing data handling methods, but also introduce quantile regression to improve the performances of PLS algorithms when the relationships among manifest and latent variables are not fixed according to the explored quantile of interest. Thus, we can get the overall view of variables’ relationships at different levels. The main challenges lie in how to introduce quantile regression in PLS algorithms correctly and how well the PLS algorithms perform when missing manifest variables occur. By simulation studies, we compare all the PLS algorithms with missing data handling methods in different settings, and finally build a business sophistication hierarchical latent variable model based on real data.
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Dissertations / Theses on the topic "Algorithmie quantique"

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Remaud, Maxime. "Applications of Quantum Fourier Sampling and the Dihedral Hidden Subgroup Problem." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS326.

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Le problème de sous-groupe caché (HSP) consiste à trouver un sous-groupe inconnu dans un groupe en utilisant une fonction constante et distincte sur les classes de ce sous-groupe. Il relève d'une grande importance en informatique théorique et en cryptographie et il s'avère que des algorithmes quantiques en résolvent efficacement certaines instances difficiles. Notamment, un HSP dans un groupe abélien se résout en temps polynomial en la taille du groupe (un fameux exemple est le problème de logarithme discret, résolu par l'algorithme de Shor). La résolution du HSP se fonde essentiellement sur la technique d'échantillonnage de Fourier quantique, qui hérite des propriétés de la transformée de Fourier quantique pour résoudre des problèmes avec périodicité. Dans cette thèse, nous introduisons un algorithme quantique pour résoudre le problème de recherche de mot de poids faible dans un code aléatoire construit à partir d'un algorithme permettant de décoder son code dual. Ceci est une adaptation en métrique de Hamming d'une réduction quantique en métrique euclidienne d'une version du problème de vecteur le plus court au problème d'apprentissage avec erreurs, qui utilise la technique d'échantillonnage de Fourier quantique et une idée due à Regev. Nous rappelons ensuite comment résoudre le HSP dans un groupe diédral (DHSP), problème auquel de nombreux autres utilisés en cryptographie post-quantique se réduisent, ainsi que la sécurité de certains cryptosystèmes, comme CSIDH par exemple. Le DHSP se réduit en fait lui-même au problème, quantique, de classes diédrales (DCP), pour lequel nous rappelons les différentes méthodes de résolution. Celles-ci se divisent en deux familles: le problème peut-être résolu de manière directe à l'aide de portes CNOT et de mesures (premier algorithme de Kuperberg), ou alors il peut-être réduit à un problème de subset-sum classique (algorithmes de Regev et deuxième de Kuperberg). Nous décrivons alors un algorithme d'une nouvelle sorte en s'inspirant des mêmes techniques qu'utilisées dans la réduction décrite précédemment, réduisant le DCP à un problème de subset-sum quantique. L'algorithme obtenu est le plus efficace en terme de requêtes à l'oracle inhérent au DCP. Une interpolation efficace en terme de requêtes entre ce nouvel algorithme et le deuxième de Kuperberg est également introduite. Enfin, nous explorons des approches alternatives pour résoudre le DCP en utilisant moins d'espace (mais potentiellement plus de requêtes à l'oracle), dans l'esprit du premier algorithme de Kuperberg<br>The hidden subgroup problem (HSP) consists in finding an unknown subgroup in a group using a constant and distinct function on the cosets of this subgroup. It is of great importance in theoretical computer science and cryptography, and it turns out that quantum algorithms effectively solve some difficult instances of it. In particular, an HSP in an abelian group can be solved in polynomial time in the size of the group (a famous example is the discrete logarithm problem, solved by Shor's algorithm). Solving the HSP is essentially based on the quantum Fourier sampling technique, which inherits the properties of the quantum Fourier transform to solve problems with periodicity. In this thesis, we introduce a quantum algorithm for solving the problem of finding the shortest codeword in a random code constructed from an algorithm for decoding its dual code. This is an adaptation in Hamming metrics of a quantum reduction in Euclidean metrics of a version of the shortest vector problem to the learning-with-errors problem, which uses the quantum Fourier sampling technique and an idea due to Regev. We then recall how to solve the HSP in a dihedral group (DHSP), a problem to which many others used in post-quantum cryptography reduce, as well as the security of certain cryptosystems, such as CSIDH for example. The DHSP is in fact itself reduced to the (quantum) dihedral coset problem (DCP), for which we recall the various methods of solution. These fall into two families: the problem can be solved directly using CNOT gates and measurements (first Kuperberg algorithm), or it can be reduced to a classical subset-sum problem (Regev and second Kuperberg algorithms). We then describe a novel algorithm, inspired by the same techniques used in the reduction described above, that reduces the DCP to a quantum subset-sum problem. The resulting algorithm is the most efficient in terms of queries to the oracle inherent to the DCP. A query efficient interpolation between this new algorithm and the second Kuperberg algorithm is also presented. Finally, we explore alternative approaches to solving the DCP using less space (but potentially more oracle queries) in the spirit of Kuperberg's first algorithm
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Lopez, Acevedo Olga Lucia. "Marches quantiques généralisées pour l'algorithmique quantique." Cergy-Pontoise, 2005. http://biblioweb.u-cergy.fr/theses/05CERG0258.pdf.

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Nous avons étudié les algorithmes quantiques dans le but de calculer le permanent d'une matrice avec une machine quantique. Après avoir construit quelques algorithmes, nous nous sommes interessés aux équivalents quantiques des marches aléatoires. Ces marches peuvent être à la base de nouveaux algorithmes quantiques. Nous avons commencé par généraliser le modèle existant et classifier les marches sur des graphes de Cayley de groupes simples. Nous avons étudié des marches sur l'hypercube et le réseau simple à une et deux directions. Pour ces graphes nous avons calculé analytiquement la fonction d'onde et exploré numériquement le temps d'arrivée et la variance. Nous avons de plus élargi deux théorèmes existants concernant l'existence des marches scalaires et la limite faible. Ces résultats nous permettent d'envisager de compléter la classification des marches pour des graphes plus complexes dans le but d'obtenir des informations structurales sur les sous-algorithmes quantiques possibles<br>We have studied quantum algorithms with the purpose of calculating a matrix permanent with a quantum computer. After constructing some algorithms, we started to study the quantum equivalent of a random walk. These walks have been introduced hoping to build new quantum algorithms from them. We started by generalizing the existing model of quantum walk and started a classification of the walks defined on Cayley graphs of the simplest groups. We studied then quantum walks over the hypercube and simple lattices in one and two dimensions and we obtained an analytical expression for the wave function, in order to explore numerically quantities such as the hitting time and the variance. Finally, we also extended two existing theorems about the existence of quantum scalar walks and about the weak limit of the walk. These results enable us to consider the classification of more complex graphs with an aim of obtaining structural information on the quantum sub-algorithms that can be constructed
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Lopez, Acevedo Olga. "Marches quantiques généralisées pour l'algorithmique quantique." Phd thesis, Université de Cergy Pontoise, 2005. http://tel.archives-ouvertes.fr/tel-00169212.

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Nous avons étudié les algorithmes quantiques dans le but de calculer le permanent d'une matrice avec une machine quantique. Après avoir construit quelques algorithmes, nous nous sommes interessés aux équivalents quantiques des marches aléatoires. Ces marches peuvent être à la base de nouveaux algorithmes quantiques. Nous avons commencé par généraliser le modèle existant et classifier les marches sur des graphes de Cayley de groupes simples. Nous avons étudié des marches sur l'hypercube et le réseau simple à une et deux directions. Pour ces graphes nous avons calculé analytiquement la fonction d'onde et exploré numériquement le temps d'arrivée et la variance. Nous avons de plus élargi deux théorèmes existants concernant l'existence des marches scalaires et la limite faible. Ces résultats nous permettent d'envisager de compléter la classification des marches pour des graphes plus complexes dans le but d'obtenir des informations structurales sur les sous-algorithmes quantiques possibles.
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Ollivier, Harold. "Eléments de théorie de l'information quantique, décohérence et codes correcteurs quantiques." Phd thesis, Ecole Polytechnique X, 2004. http://pastel.archives-ouvertes.fr/pastel-00001131.

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Depuis 20 ans, l'information quantique a profondément changé notre façon d'appréhender la physique atomique, ainsi que la nature des ressources utiles au calcul. Cette thèse aborde trois aspects relatifs à l'information quantique: - Le phénomène de décohérence -- responsable de la transition quantique-classique -- est décrit quantitativement grâce à l'analyse de l'information mutuelle entre deux systèmes quantiques ; - Une nouvelle classe de codes correcteurs d'erreurs quantiques -- les codes convolutifs -- est introduite en detail et il est montré qu'elle partage les propriétés des codes convolutifs classiques (codage et décodage en ligne, algorithme efficace d'estimation d'erreurs au maximum de vraisemblance, existence de condition nécessaire et suffisante pour l'absence d'erreur catastrophique, etc.) ; - Quelques propositions expérimentales de manipulation d'information quantique sont décrites (porte de Toffoli et clonage universel pour l'électrodynamique quantique en cavité).
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Grospellier, Antoine. "Décodage des codes expanseurs quantiques et application au calcul quantique tolérant aux fautes." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS575.

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Le calcul quantique tolérant aux fautes est un ensemble de techniques dont le but est d'effectuer des calculs quantiques de manière fiable en utilisant des composants bruités. Dans ce contexte, l'utilisation de codes correcteurs quantiques maintient le nombre d'erreurs présentes dans le système en dessous d'un seuil tolérable. L'un des principaux problèmes de ce domaine est d'évaluer le coût minimum (en mémoire et en temps) nécessaire pour transformer un calcul quantique idéal en un calcul tolérant aux fautes. Dans cette thèse, nous montrons que la famille des codes expanseurs quantiques associée à l'algorithme de décodage small-set-flip peut être utilisée dans la construction de ref. [arXiv:1310.2984] pour réaliser du calcul quantique tolérant aux fautes avec coût constant en mémoire. La famille de codes correcteurs ainsi que le décodeur que nous étudions ont été introduits dans ref. [arXiv:1504.00822] où un modèle de bruit adverse est considéré. En nous appuyant sur les résultats de cet article, nous analysons le comportement des codes expanseurs quantiques face à un modèle de bruit stochastique qui est pertinent dans le cadre du calcul tolérant aux fautes [arXiv:1711.08351], [arXiv:1808.03821]. De plus, nous montrons que l'algorithme de décodage peut être parallélisé pour fonctionner en temps constant. Cette propriété est essentielle pour éviter que les erreurs ne s'accumulent pendant que l'algorithme est exécuté. Au-delà des résultats théoriques décrits ci-dessus, nous avons effectué une analyse numérique des codes expanseurs quantiques dans le but d'évaluer leurs performances en pratique [arXiv:1810.03681]. Le modèle de bruit choisi pour ces simulations consiste à générer des erreurs de types X et Z de manière indépendante et identiquement distribuée sur les qubits. Les résultats obtenus pour ces codes de rendement constant sont prometteurs puisque nos simulations montrent que leur seuil est décent et que leurs performances à taille finie sont bonnes<br>Fault tolerant quantum computation is a technique to perform reliable quantum computation using noisy components. In this context, quantum error correcting codes are used to keep the amount of errors under a sustainable threshold. One of the main problems of this field is to determine the minimum cost, in terms of memory and time, which is needed in order to transform an ideal quantum computation into a fault-tolerant one. In this PhD thesis, we show that the family of quantum expander codes and the small-set-flip decoder can be used in the construction of ref. [arXiv:1310.2984] to produce a fault-tolerant quantum circuit with constant space overhead. The error correcting code family and the decoder that we study has been introduced in ref. [arXiv:1504.00822] where an adversarial error model was examined. Based on the results of this article, we analyze quantum expander codes subjected to a stochastic error model which is relevant for fault-tolerant quantum computation [arXiv:1711.08351], [arXiv:1808.03821]. In addition, we show that the decoding algorithm can be parallelized to run in constant time. This is very relevant to prevent errors from accumulating while the decoding algorithm is running. Beyond the theoretical results described above, we perform a numerical analysis of quantum expander codes to measure their performance in practice [arXiv:1810.03681]. The error model used during these simulations generates X and Z type errors on the qubits with an independent and identically distributed probability distribution. Our results are promising because they reveal that these constant rate codes have a decent threshold and good finite length performance
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Mhalla, Mehdi. "Informatique quantique, algorithmes et complexité." Grenoble INPG, 2004. http://www.theses.fr/2004INPG0113.

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Nous présentons dans ce travail plusieurs résultats dans différents domaines de l'information quantique. Après une première partie où nous proposons une introduction au domaine, nous proposons des caractérisations simples et efficaces du phénomène de l'intrication des états purs. Les deux formes de séparabilité étudiées sont la séparabilité totale et la p-q séparabilité. Ces caractérisations nous ont permis de donner des algorithmes optimaux de détection de la séparabilité ayant un gain quadratrique par rapport aux méthodes connues. La troisième partie s'intéresse aux jeux quantiques. Nous présentons tout d'abord une analyse fine des jeux octaux classiques et donnons une stratégie optimale pour le jeu des dominos. Nous proposons ensuite une quantisation de certains jeux combinatoires, définissant ainsi la famille des jeux octaux quantiques. Nous présentons ainsi un modèle formel permettant de parler des jeux quantiques à information totale et proposons une approche qui utilise des pièges pour des jeux quantiques très étudiés, appelés jeux de dés à distance. La dernière partie étudie des problèmes d'optimisation, en développant pour cela des outils optimaux de recherche de minima. Ces outils nous ont permis d'analyser la complexité en requêtes de certains problèmes de graphes. Nous avons ainsi trouvé des algorithmes quantiques pour les problèmes de connectivité, forte connectivité, arbre couvrant de poids minimal et plus courts chemins à une source donnée. Puis, nous avons prouvé leur optimalité en utilisant des techniques de bornes inférieures, déterminant ainsi les limites du facteur de gain qu'offre l'informatique quantique pour résoudre ce problème<br>This work consists in several results in different domains of quantum computing. First, we propose an introduction to the quantum computing theory. Then we give efficient characterizations of entanglement for pure states. We define the full separability and the p-q separability, and give optimal algorithms that improve by a quadratic factor the detection of entanglement. The third part is dedicated to quantum game theory. We analyse some classical combinatorial games, and find an optimal strategy for the 0. 07 octal game. Then we propose a quantisation of the family of octal games, and of some other combinatorial games, defining by the way a formalism that permits to study such games. We also provide some new ideas for the study of the well know coin flip game. In the last part, we study optimisation problems, and give an optimal minima finding algorithm based on the quantum search. Then we apply this tool to design algorithms for some graph problems (connectivity, strong connectivity, minimum spanning tree and single source shortest paths. We prove the optimality of our algorithms by using the quantum adversary lower bound method, giving therefore a characherisation of the speed-up given by quantum computing for these problems
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Javelle, Jérôme. "Cryptographie Quantique : Protocoles et Graphes." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM093/document.

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Je souhaite réaliser un modèle théorique optimal pour les protocoles de partage de secret quantique basé sur l'utilisation des états graphes. Le paramètre représentatif d'un partage de secret à seuil est, entre autres la taille du plus grand ensemble de joueurs qui ne peut pas accéder au secret. Je souhaite donc trouver un famille de protocoles pour laquelle ce paramètre est le plus petit possible. J'étudie également les liens entre les protocoles de partage de secret quantique et des familles de courbes en géométrie algébrique<br>I want to realize an optimal theoretical model for quantum secret sharing protocols based on graph states. The main parameter of a threshold quantum secret sharing scheme is the size of the largest set of players that can not access the secret. Thus, my goal is to find a collection of protocols for which the value of this parameter is the smallest possible. I also study the links between quantum secret sharing protocols and families of curves in algebraic geometry
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Schrottenloher, André. "Quantum Algorithms for Cryptanalysis and Quantum-safe Symmetric Cryptography." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS271.

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La cryptographie moderne est fondée sur la notion de sécurité computationnelle. Les niveaux de sécurité attendus des cryptosystèmes sont exprimés en nombre d'opérations ; une attaque est un algorithme d'une complexité inférieure à la borne attendue. Mais ces niveaux de sécurité doivent aujourd'hui prendre en compte une nouvelle notion d'algorithme : le paradigme du calcul quantique. Dans le même temps,la délégation grandissante du chiffrement à des puces RFID, objets connectés ou matériels embarqués pose de nouvelles contraintes de coût.Dans cette thèse, nous étudions la sécurité des cryptosystèmes à clé secrète face à un adversaire quantique.Nous introduisons tout d'abord de nouveaux algorithmes quantiques pour les problèmes génériques de k-listes (k-XOR ou k-SUM), construits en composant des procédures de recherche exhaustive.Nous présentons ensuite des résultats de cryptanalyse dédiée, en commençant par un nouvel outil de cryptanalyse quantique, l'algorithme de Simon hors-ligne. Nous décrivons de nouvelles attaques contre les algorithmes Spook et Gimli et nous effectuons la première étude de sécurité quantique du chiffrement AES. Dans un troisième temps, nous spécifions Saturnin, une famille de cryptosystèmes à bas coût orientés vers la sécurité post-quantique. La structure de Saturnin est proche de celle de l'AES et sa sécurité en tire largement parti<br>Modern cryptography relies on the notion of computational security. The level of security given by a cryptosystem is expressed as an amount of computational resources required to break it. The goal of cryptanalysis is to find attacks, that is, algorithms with lower complexities than the conjectural bounds.With the advent of quantum computing devices, these levels of security have to be updated to take a whole new notion of algorithms into account. At the same time, cryptography is becoming widely used in small devices (smart cards, sensors), with new cost constraints.In this thesis, we study the security of secret-key cryptosystems against quantum adversaries.We first build new quantum algorithms for k-list (k-XOR or k-SUM) problems, by composing exhaustive search procedures. Next, we present dedicated cryptanalysis results, starting with a new quantum cryptanalysis tool, the offline Simon's algorithm. We describe new attacks against the lightweight algorithms Spook and Gimli and we perform the first quantum security analysis of the standard cipher AES.Finally, we specify Saturnin, a family of lightweight cryptosystems oriented towards post-quantum security. Thanks to a very similar structure, its security relies largely on the analysis of AES
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Sanselme, Luc. "Algorithmes quantiques dans les groupes nilpotents." Paris 11, 2008. http://www.theses.fr/2008PA112297.

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Dans cette thèse, nous commençons par donner avec précision une définition formelle des groupes boîtes noires, et nous rappelons les principaux algorithmes existant dans ce cadre. Dans un deuxième temps, nous proposons une définition nouvelle d’un groupe boîte noire quantique. Nous formalisons, par ailleurs, précisément cette définition et donnons les principaux algorithmes quantiques connus dans ce cadre. Ensuite, nous donnons un certain nombre d’algorithmes de calcul de théorie algorithmique quantique des groupes dans les groupes résolubles, et dans certaines sous-classes particulières de ces groupes. Enfin, nous présentons un résultat original, démontré au cours de l’élaboration de cette thèse. Nous expliquons comment résoudre efficacement le problème du sous-groupe caché dans les groupes extraspéciaux et nilpotents de classe deux, en calcul quantique. Au passage, nous donnons un certain nombre de réductions du problème du sous-groupe caché, valable dans un groupe nilpotent de classe supérieure. Le dernier chapitre, un peu à part dans cette thèse, explique comment résoudre efficacement un système d’équations quadratiques dans un corps fini, résultat nécessaire pour résoudre le problème du sous-groupe caché dans les groupes nilpotents de classe 2<br>We start off this Ph. D. Thesis with giving the definition of a black-box group and reminding some algorithm associated with this group representation. Then, we put forward a new definition of a quantum black-box group. We explain precisely this new approach and we enumerate the main algorithms associated to this notion. After that, we give some algorithm of quantum computational group theory in solvable groups and in some subclasses of these solvable groups such as nilpotent groups, p-groups or extraspecial groups. Finally, we present a new result that was proved during this thesis. We show that we can solve efficiently, with a quantum computer, the hidden subgroup problem in extraspecial and nilpotent group of class 2. In addition, we give some reduction of the Hidden subgroup problem in nilpotent groups of higher classes. The last chapter of this thesis shows how to solve some system of quadratic equations over a finite field. This result is needed to solve the Hidden subgroup problem in nilpotent groups of class 2
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Tapp, Alain. "Informatique quantique, algorithmes et complexité de la communication." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0018/NQ51978.pdf.

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Books on the topic "Algorithmie quantique"

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Koenker, Roger W. An interior point algorithm for nonlinear quantile regression / Roger Koenker ; Beum J. Park. University of Illinois at Urbana-Champaign, 1992.

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Chout, Philippe. Théorie du Hasard Prévisible: Heuristique Quantique des Algorithmes Stochastiques. Independently Published, 2020.

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Chout, Philippe. Heuristique Quantique des Algorithmes Stochastiques: Comment Dominer le Hasard Au Jeu de la Roulette. Independently Published, 2019.

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Hasard, Domine Le. Comment Dominer le Hasard Au Jeu de la Roulette: Heuristique Quantique des Algorithmes Stochastiques. Independently Published, 2019.

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Hasard, Domine Le. Heuristique Quantique des Algorithmes Stochastiques: Comment Dominer le Hasard Au Jeu de la Roulette. Independently Published, 2019.

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Book chapters on the topic "Algorithmie quantique"

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Yu, Chun-Nam, Michael Crouch, Ruichuan Chen, and Alessandra Sala. "Online Algorithm for Approximate Quantile Queries on Sliding Windows." In Experimental Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38851-9_25.

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Sharma, Dreamlee, and Tapan Kumar Chakrabarty. "A Quantile-Based Approach to Supervised Learning." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3357-0_21.

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Chen, C. "An Adaptive Algorithm for Quantile Regression." In Theory and Applications of Recent Robust Methods. Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_4.

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Panjwani, Shweta, S. Naresh Kumar, and Laxmi Ahuja. "Bias Correction of GCM Data Using Quantile Mapping Technique." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5077-5_55.

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Joseph, Ajin George, and Shalabh Bhatnagar. "A Stochastic Approximation Algorithm for Quantile Estimation." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26535-3_36.

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Chrétien, Stéphane, Oya Ekin Karaşan, Ecenur Oguz, and Mustafa Ç. Pınar. "The Quantile Matching Problem and Point Cloud Registration." In SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21). Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976830.2.

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Xue, Zhengyuan. "An Effective Single-Pass Approach for Estimating the Φ-quantile in Data Streams." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95384-3_48.

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Schiefer, Nicholas, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, and Tal Wagner. "Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees." In SIAM Conference on Applied and Computational Discrete Algorithms (ACDA23). Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977714.8.

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Giacobbe, Mirco, Thomas A. Henzinger, and Mathias Lechner. "How Many Bits Does it Take to Quantize Your Neural Network?" In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45237-7_5.

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Abstract Quantization converts neural networks into low-bit fixed-point computations which can be carried out by efficient integer-only hardware, and is standard practice for the deployment of neural networks on real-time embedded devices. However, like their real-numbered counterpart, quantized networks are not immune to malicious misclassification caused by adversarial attacks. We investigate how quantization affects a network’s robustness to adversarial attacks, which is a formal verification question. We show that neither robustness nor non-robustness are monotonic with changing the number of bits for the representation and, also, neither are preserved by quantization from a real-numbered network. For this reason, we introduce a verification method for quantized neural networks which, using SMT solving over bit-vectors, accounts for their exact, bit-precise semantics. We built a tool and analyzed the effect of quantization on a classifier for the MNIST dataset. We demonstrate that, compared to our method, existing methods for the analysis of real-numbered networks often derive false conclusions about their quantizations, both when determining robustness and when detecting attacks, and that existing methods for quantized networks often miss attacks. Furthermore, we applied our method beyond robustness, showing how the number of bits in quantization enlarges the gender bias of a predictor for students’ grades.
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Das, N. C. "Bivariate Normal Distribution and Heuristic-Algorithm of BIVNOR for Generating Biquantile Pairs." In Decision Processes by Using Bivariate Normal Quantile Pairs. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2364-1_4.

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Conference papers on the topic "Algorithmie quantique"

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Silveira, Eduardo, Joaquim Assunção, and Leonardo Emmendorfer. "Quantile Symbolic Aggregate approXimation: A guaranteed equiprobable SAX." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbbd.2023.232421.

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Time series are broadly present in science and industry. In specific scenarios, it is useful to classify series in order to gain knowledge regarding a specific range of values. In such cases, we often use symbolic representation, as it can reduce the data dimensionality creating representative symbols, making the data discrete and allowing specialized algorithms to be applied to the data. One of the most prominent methods of this type of representation is the Symbolic Aggregate approXimation (SAX), which, in addition to generating the symbolic sequence, also reduces the data dimension. However, one of the problems of SAX is that, in order to guarantee the balance of symbols, it assumes the normality of the distribution, which fails in some distributions and causes the class imbalance problem. We propose a unique seamless approach to guarantee the balance among the classes, which may lead to better performance in classification algorithms.
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Xiao, Leibing, Xinchao Wei, Yuelei Xu, et al. "Truncated Quantile Critics Algorithm for Cryptocurrency Portfolio Optimization." In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023. http://dx.doi.org/10.1109/smc53992.2023.10393867.

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Yang, Ping, Bing Xiao, Xin Chen, and Liangliang Guo. "Interval quantile maximum mutual information feature selection algorithm." In CCEAI 2023: 2023 7th International Conference on Control Engineering and Artificial Intelligence. ACM, 2023. http://dx.doi.org/10.1145/3580219.3580234.

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Huang, Zengfeng, Lu Wang, Ke Yi, and Yunhao Liu. "Sampling based algorithms for quantile computation in sensor networks." In the 2011 international conference. ACM Press, 2011. http://dx.doi.org/10.1145/1989323.1989401.

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Haeupler, Bernhard, Jeet Mohapatra, and Hsin-Hao Su. "Optimal Gossip Algorithms for Exact and Approximate Quantile Computations." In PODC '18: ACM Symposium on Principles of Distributed Computing. ACM, 2018. http://dx.doi.org/10.1145/3212734.3212770.

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Ramirez, M., E. Tapia, M. Block, and R. Rojas. "Quantile Linear Algorithm for Robust Binarization of Digitalized Letters." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2. IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4377097.

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Yang, Bei, Houkuan Huang, and Zhihai Wang. "An Efficient Algorithm for Quantile Computation over Streaming Data." In 2007 Second International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2007. http://dx.doi.org/10.1109/bicta.2007.4806436.

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Huang, Siyu, and Tong Tian. "Prediction of Mechanical Properties of Hot Rolled Strip Based on DBN and Composite Quantile Regression." In ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence. ACM, 2021. http://dx.doi.org/10.1145/3508546.3508656.

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Malik, Jahan, Punit Kumar Shet, Dhanush Gc, Naren Karri, B. H. Kushal, and Abhinay Kumar Singh. "Road Surface recognition by using Quantile Transformation and ML Algorithms." In 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). IEEE, 2022. http://dx.doi.org/10.1109/gcat55367.2022.9971969.

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Cui, Yufei, Ziquan Liu, Wuguannan Yao, et al. "Fully Nested Neural Network for Adaptive Compression and Quantization." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/288.

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Neural network compression and quantization are important tasks for fitting state-of-the-art models into the computational, memory and power constraints of mobile devices and embedded hardware. Recent approaches to model compression/quantization are based on reinforcement learning or search methods to quantize the neural network for a specific hardware platform. However, these methods require multiple runs to compress/quantize the same base neural network to different hardware setups. In this work, we propose a fully nested neural network (FN3) that runs only once to build a nested set of compressed/quantized models, which is optimal for different resource constraints. Specifically, we exploit the additive characteristic in different levels of building blocks in neural network and propose an ordered dropout (ODO) operation that ranks the building blocks. Given a trained FN3, a fast heuristic search algorithm is run offline to find the optimal removal of components to maximize the accuracy under different constraints. Compared with the related works on adaptive neural network designed only for channels or bits, the proposed approach is applicable to different levels of building blocks (bits, neurons, channels, residual paths and layers). Empirical results validate strong practical performance of proposed approach.
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Reports on the topic "Algorithmie quantique"

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Over, Thomas, Riki Saito, Andrea Veilleux, et al. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.
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