Quantum state transfer with untuneable couplings

We present a general scheme for implementing bi-directional quantum state transfer in a quantum swapping channel. Unlike many other schemes for quantum computation and communication, our method does not require qubit couplings to be switched on and off. The only control variable is the bias acting on individual qubits. We show how to derive the parameters of the system (fixed and variable) such that perfect state transfer can be achieved. Since these parameters vary linearly with the pulse width, our scheme allows flexibility in the time scales under which qubits evolve. Unlike quantum spin networks, our scheme allows the transmission of several quantum states at a time, requiring only a two qubit separation between quantum states. By pulsing the biases of several qubits at the same time, we show that only eight bias control lines are required to achieve state transfer along a channel of arbitrary length. Furthermore, when the information to be transferred is purely classical in nature, only three bias control lines are required, greatly simplifying the circuit complexity

Constraints on Low-Mass WIMP Interactions on 19F from PICASSO

Recent results from the PICASSO dark matter search experiment at SNOLAB are reported. These results were obtained using a subset of 10 detectors with a total target mass of 0.72 kg of 19F and an exposure of 114 kgd. The low backgrounds in PICASSO allow recoil energy thresholds as low as 1.7 keV to be obtained which results in an increased sensitivity to interactions from Weakly Interacting Massive Particles (WIMPs) with masses below 10 GeV/c^2. No dark matter signal was found. Best exclusion limits in the spin dependent sector were obtained for WIMP masses of 20 GeV/c^2 with a cross section on protons of sigma_p^SD = 0.032 pb (90% C.L.). In the spin independent sector close to the low mass region of 7 GeV/c2 favoured by CoGeNT and DAMA/LIBRA, cross sections larger than sigma_p^SI = 1.41x10^-4 pb (90% C.L.) are excluded.Comment: 23 pages, 7 figures, to be published in Phys. Lett.

Electron/pion separation with an Emulsion Cloud Chamber by using a Neural Network

We have studied the performance of a new algorithm for electron/pion separation in an Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The software for separation consists of two parts: a shower reconstruction algorithm and a Neural Network that assigns to each reconstructed shower the probability to be an electron or a pion. The performance has been studied for the ECC of the OPERA experiment [1]. The $e/\pi$ separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data taken at CERN (pion beams) and at DESY (electron beams). The algorithm allows to achieve a 90% electron identification efficiency with a pion misidentification smaller than 1% for energies higher than 2 GeV