Ergodicity in randomly perturbed quantum systems

The theoretical cornerstone of statistical mechanics is the ergodic assumption that all accessible configurations of a physical system are equally likely. Here we show how such property arises when an open quantum system is continuously perturbed by an external environment effectively observing the system at random times while the system dynamics approaches the quantum Zeno regime. In this context, by large deviation theory we analytically show how the most probable value of the probability for the system to be in a given state eventually deviates from the non-stochastic case when the Zeno condition is not satisfied. We experimentally test our results with ultra-cold atoms prepared on an atom chip.Comment: 6 pages, 5 figure

Experimental realization of quantum zeno dynamics

It is generally impossible to probe a quantum system without disturbing it. However, it is possible to exploit the back-action of quantum measurements and strong couplings to tailor and protect the coherent evolution of a quantum system. This is a profound and counterintuitive phenomenon known as quantum Zeno dynamics (QZD). Here we demonstrate QZD with a rubidium Bose-Einstein condensate in a five-level Hilbert space. We harness measurements and strong couplings to dynamically disconnect different groups of quantum states and constrain the atoms to coherently evolve inside a two-level subregion. In parallel to the foundational importance due to the realization of a dynamical superselection rule and the theory of quantum measurements, this is an important step forward in protecting and controlling quantum dynamics and, broadly speaking, quantum information processing.Comment: 7 pages, 6 figure

A large atoms number magnetic trap for BEC production

This thesis describes an experimental apparatus and the operative procedures leading to load a population of 87Rb atoms in a magnetic trap. The machine used is devoted to the high rate production of Bose-Eintein Condensates (BECs). An integral part of the system is the magneto-optical trap (MOT), where a cloud of atoms can be trapped and cooled by a spatially modulated force from atom-photon momentum exchange. Our experiment is based on a double MOT apparatus where a ve beams MOT acts as a source of slow atoms for a second six beam MOT in the Ultra High Vacuum (UHV) region. The light with the necessary properties to slow the atoms (power stability, spectral purity, spatial homogeneity) is produced with a Master Oscillator Power Amplier (MOPA) laser system. The resulting power for each beam used for the MOTs exceeds the saturation intensity of the F = 2 -> F' = 3 transitions (saturated MOT). The number of atoms trapped with this set-up is of the order of 10^8. The magnetic trap setup is a quadrupole and Ioe trap conguration (QUIC trap), which consists of a pairs of coils in anti-Helmoltz conguration and an Ioffe coil. They can produce a magnetic eld gradient up to 500 G/cm and allow easy optical access to the experiment. Magneto-optically trapped atoms are compressed, cooled by the optical molasses technique, and then optically pumped into the |F_g = 2;m_F = 2> state before being loaded into the quadrupole trap. With a transfer effciency of 52 percent from the second MOT into the magnetic trap, this setup is capable of maintain the trapped atoms for a time of 45+/-4 s. The atomic cloud released from the magnetic trap is probed by absorption imaging to determine the number of trapped atoms. The next steps in order to produce a BEC are the changing of the trap conguration to the QUIC trap and the RF evaporative cooling process, to force the condensation of trapped atoms

Calendar based forecast of emergency department visits

In this paper we use a well established method for short-termforecasting to predict the amount of hourly Emergency Department (ED)visits in thirteen different hospitals in the south-east area of Tuscany. Ouralgorithm belongs to the class of similar shape algorithms and performthe forecast in an unsupervised manner. It exploit an historical datasetcontaining the patient arrival data, in which similar pattern, filtered onthe base of a calendar condition, are selected to predict the incomingvisit volume for a tunable number of day ahead