Quantum-enhanced reinforcement learning for finite-episode games with discrete state spaces

Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. Here, we present a way to partially embed both Monte Carlo policy iteration for finding an optimal policy on random observations, as well as how to embed (n) sub-optimal state-value functions for approximating an improved state-value function given a policy for finite horizon games with discrete state spaces on a D-Wave 2000Q quantum processing unit (QPU). We explain how both problems can be expressed as a quadratic unconstrained binary optimization (QUBO) problem, and show that quantum-enhanced Monte Carlo policy evaluation allows for finding equivalent or better state-value functions for a given policy with the same number episodes compared to a purely classical Monte Carlo algorithm. Additionally, we describe a quantum-classical policy learning algorithm. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical policy evaluation algorithm.Comment: 17 pages, 7 figure

A new CDF model for data movement based on SRM

Being a large international collaboration established well before the full development of the Grid as the main computing tool for High Energy Physics, CDF has recently changed and improved its computing model, decentralizing some parts of it in order to be able to exploit the rising number of distributed resources available nowadays. Despite those efforts, while the large majority of CDF Monte Carlo production has moved to the Grid, data processing is still mainly performed in dedicated farms hosted at FNAL, requiring a centralized management of data and Monte Carlo samples needed for physics analysis. This rises the question on how to manage the transfer of produced Monte Carlo samples from remote Grid sites to FNAL in an efficient way; up to now CDF has relied on a non scalable centralized solution based on dedicated data servers accessed through rcp protocol, which has proven to be unsatisfactory. A new data transfer model has been designed that uses SRMs as local caches for remote Monte Carlo production sites, interfaces them with SAM, the experiment data catalog, and finally realizes the file movement exploiting the features provided by the data catalog transfer layer. We describe here the model and its integration within the current CDF computing architecture

Measurement of the ttbar production cross section in the MET+jets channel at CDF

This thesis is focused on an inclusive search of the t{bar t} {yields} E{sub T} + jets decay channel by means of neural network tools in proton antiproton collisions at {radical}s = 1.96 TeV recorded by the Collider Detector at Fermilab (CDF). At the Tevatron p{bar p} collider top quarks are mainly produced in pairs through quark-antiquark annihilation and gluon-gluon fusion processes; in the Standard Model description, the top quark then decays to a W boson and a b quark almost 100% of the times, so that its decay signatures are classified according to the W decay modes. When only one W decays leptonically, the t{bar t} event typically contains a charged lepton, missing transverse energy due to the presence of a neutrino escaping from the detector, and four high transverse momentum jets, two of which originate from b quarks. In this thesis we describe a t{bar t} production cross section measurement which uses data collected by a 'multijet' trigger, and selects this kind of top decays by requiring a high-P{sub T} neutrino signature and by using an optimized neural network to discriminate top quark pair production from backgrounds. In Chapter 1, a brief review of the Standard Model of particle physics will be discussed, focusing on top quark properties and experimental signatures. In Chapter 2 will be presented an overview of the Tevatron accelerator chain that provides p{bar p} collisions at the center-of-mass energy of {radical}s = 1.96 TeV, and proton and antiproton beams production procedure will be discussed. The CDF detector and its components and subsystems used for the study of p{bar p} collisions provided by the Tevatron will be described in Chapter 3. Chapter 4 will detail the reconstruction procedures used in CDF to detect physical objects exploiting the features of the different detector subsystems. Chapter 5 will provide an overview of the main concepts regarding Artificial Neural Networks, one of the most important tools we will use in the analysis. Chapter 6 will be devoted to the description of the main characteristics of the t{bar t} {yields} E{sub T} + jets decay channel used to train our neural network to discriminate the top pair production from background processes. We will discuss the event selection method and the technique used for background prediction, that will rely on b-jets identification rate parameterization. Finally, Chapter 7 will provide a description of the final data sample and a detailed discussion of the systematic uncertainties before determining the cross section measurement by means of a likelihood maximization