Classical Simulation of Quantum Fields II

We consider the classical time evolution of a real scalar field in 2 dimensional Minkowski space with a $\lambda \phi^4$ interaction. We compute the spatial and temporal two-point correlation functions and extract the renormalized mass of the interacting theory. We find our results are consistent with the one- and two-loop quantum computation. We also perform Monte Carlo simulations of the quantum theory and conclude that the classical scheme is able to produce more accurate results with a fraction of the CPU time.Comment: 16 pages, 8 figures, now matches published versio

Towards Holography via Quantum Source-Channel Codes

While originally motivated by quantum computation, quantum error correction (QEC) is currently providing valuable insights into many-body quantum physics, such as topological phases of matter. Furthermore, mounting evidence originating from holography research (AdS/CFT) indicates that QEC should also be pertinent for conformal field theories. With this motivation in mind, we introduce quantum source-channel codes, which combine features of lossy compression and approximate quantum error correction, both of which are predicted in holography. Through a recent construction for approximate recovery maps, we derive guarantees on its erasure decoding performance from calculations of an entropic quantity called conditional mutual information. As an example, we consider Gibbs states of the transverse field Ising model at criticality and provide evidence that they exhibit nontrivial protection from local erasure. This gives rise to the first concrete interpretation of a bona fide conformal field theory as a quantum error correcting code. We argue that quantum source-channel codes are of independent interest beyond holography

Autocovariance Estimation in the Presence of Changepoints

This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule-Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints $m$ and the series length $N$ satisfy $m/N \rightarrow 0$ as $N \rightarrow \infty$

Recherche d'inégalités oracles pour des problèmes inverses

We consider in this thesis the statistical linear inverse problem $Y = Af+ \epsilon \xi$ where $A$ denotes a compact operator, $\epsilon>0$ a noise level and $\xi$ a Gaussian white noise. The unknown function f has to be recovered from the indirect measurement Y . Given a family $\Lambda$, an oracle inequality compares the performances of an adaptive estimator $f^{\star}$ to the best one in $\Lambda$. Such an inequality is non-asymptotic and no specific informations on $f$ are required. In this thesis, we propose different oracle inequalities in order to provide both a better understanding of regularization with a noisy operator and ageneralization of the risk hull minimization (RHM) algorithm. For most of the existing methods, the operator A is assumed to be exactly known. This assumption is of major importance and may not be statisfied in many situations. In a first time, we extend the penalized blockwise Stein's rule and the risk hull minimization algorithm performances to this situation. The RHM method has been initiated by L. Cavalier and Y. Golubev. It significantly improves the performances of the traditionnal unbiased risk estimation procedure. However, this algorithm only concerns projection estimation which is rather rough. There exist several regularization approaches with better performances. We may mention for instance the Tikhonov estimators or the Landweber iterative procedure. Hence, generalization of the RHM algorithm to a wide family of linear estimators may produce interesting results. This is the aim of the last part of this thesis.Cette thèse s'intéresse aux problèmes inverses dans un cadre statistique. A partir des observations $Y=Af+\epsilon \xi$, le but est d'approximer aussi fidèlement que possible la fonction f où $A$ représente un opérateur compact, $\epsilon>0$ le niveau de bruit et $\xi$ un bruit blanc gaussien. Etant données une procédure $f^{\star}$ et une collection d'estimateurs $\Lambda$, une inégalitéoracle permet de comparer, sans aucune hypothèse sur la fonction cible $f$ et d'un point de vue non-asymptotique, les performances de $f^{\star}$ à celles du meilleur estimateur dans $\Lambda$connaissant $f$. Dans l'optique d'obtenir de telles inégalités, cette thèse s'articule autour de deux objectifs: une meilleure compréhension des problèmes inverses lorsque l'opérateur estmal-connu et l'extension de l'algorithme de minimisation de l'enveloppe du risque (RHM) à un domaine d'application plus large. La connaissance complète de l'opérateur A est en effet une hypothèse implicite dans la plupart des méthodes existantes. Il est cependant raisonnable de penser que ce dernier puisse être en partie, voire totalement inconnu. Dans un premier temps, nous généralisons donc la méthode de Stein par blocs pénalisée ainsi que l'algorithme RHM à cette situation. Ce dernier, initié par L. Cavalier et Y. Golubev, améliore considérablement les performances de la traditionnelle méthode d'estimation du risque sans biais. Cependant, cette nouvelle procédure ne concerne que les estimateurs par projection. En pratique, ces derniers sont souvent moins performants que les estimateurs de Tikhonov ou les procédures itératives, dans un certain sens beaucoup plus fines. Dans la dernière partie, nous étendons donc l'utilisation de l'enveloppe du risque à une gamme beaucoup plus large d'estimateurs

A permutation test for umbrella alternatives

There is a wide variety of stochastic ordering problems where K groups (typically ordered with respect to time) are observed along with a (continuous) response. The interest of the study may be on finding the change-point group, i.e. the group where an inversion of trend of the variable under study is observed. A change point is not merely a maximum (or a minimum) of the time-series function, but a further requirement is that the trend of the time-series is monotonically increasing before that point, and monotonically decreasing afterwards. A suitable solution can be provided within a conditional approach, i.e. by considering some suitable nonparametric combination of dependent tests for simple stochastic ordering problems. The proposed procedure is very flexible and can be extended to trend and/or repeated measure problems. Some comparisons through simulations and examples with the well known Mack & Wolfe test for umbrella alternative and with Page's test for trend problems with correlated data are investigated

Approximating the 0-1 Multiple Knapsack Problem with Agent Decomposition and Market Negotiation

The 0-1 multiple knapsack problem appears in many domains from financial portfolio management to cargo ship stowing. Methods for solving it range from approximate algorithms, such as greedy algorithms, to exact algorithms, such as branch and bound. Approximate algorithms have no bounds on how poorly they perform and exact algorithms can suffer from exponential time and space complexities with large data sets. This paper introduces a market model based on agent decomposition and market auctions for approximating the 0-1 multiple knapsack problem, and an algorithm that implements the model (M(x)). M(x) traverses the solution space rather than getting caught in a local maximum, overcoming an inherent problem of many greedy algorithms. The use of agents ensures that infeasible solutions are not considered while traversing the solution space and that traversal of the solution space is not just random, but is also directed. M(x) is compared to a bound and bound algorithm (BB) and a simple greedy algorithm with a random shuffle (G(x)). The results suggest that M(x) is a good algorithm for approximating the 0-1 Multiple Knapsack problem. M(x) almost always found solutions that were close to optimal in a fraction of the time it took BB to run and with much less memory on large test data sets. M(x) usually performed better than G(x) on hard problems with correlated data