Control of Four-Level Quantum Coherence via Discrete Spectral Shaping of an Optical Frequency Comb

We present an experiment demonstrating high-resolution coherent control of a four-level atomic system in a closed (diamond) type configuration. A femtosecond frequency comb is used to establish phase coherence between a pair of two-photon transitions in cold Rb atoms. By controlling the spectral phase of the frequency comb we demonstrate the optical phase sensitive response of the diamond system. The high-resolution state selectivity of the comb is used to demonstrate the importance of the signs of dipole moment matrix elements in this type of closed-loop excitation. Finally, the pulse shape is optimized resulting in a 256% increase in the two-photon transition rate by forcing constructive interference between the mode pairs detuned from an intermediate resonance.Comment: 5 pages, 4 figures Submitted to Physical Review Letter

Quantum nonlocality of four-qubit entangled states

Quantum nonlocality of several four-qubit states is investigated by constructing a new Bell inequality. These include the Greenberger-Zeilinger-Horne (GHZ) state, W state, cluster state, and the state $|\chi>$ that has been recently proposed in [PRL, {\bf 96}, 060502 (2006)]. The Bell inequality is optimally violated by $|\chi>$ but not violated by the GHZ state. The cluster state also violates the Bell inequality though not optimally. The state $|\chi>$ can thus be discriminated from the cluster state by using the inequality. Different aspects of four-partite entanglement are also studied by considering the usefulness of a family of four-qubit mixed states as resources for two-qubit teleportation. Our results generalize those in [PRL, {\bf 72}, 797 (1994)].Comment: 13 pages, 1 figur

Hydrogen desorption and adsorption measurements on graphite nanofibers

Graphite nanofibers were synthesized and their hydrogen desorption and adsorption properties are reported for 77 and 300 K. Catalysts were made by several different methods including chemical routes, mechanical alloying, and gas condensation. The nanofibers were grown by passing ethylene and H2 gases over the catalysts at 600 °C. Hydrogen desorption and adsorption were measured using a volumetric analysis Sieverts' apparatus, and the graphite nanofibers were characterized by transmission electron microscopy and Brunauer–Emmett–Teller surface area analysis. The absolute level of hydrogen desorption measured from these materials was typically less than the 0.01 H/C atom, comparable to other forms of carbon

Evaluation of heating effects on atoms trapped in an optical trap

We solve a stochastic master equation based on the theory of Savard et al. [T. A. Savard. K. M. O'Hara, and J. E. Thomas, Phys, Rev. A 56, R1095 (1997)] for heating arising from fluctuations in the trapping laser intensity. We compare with recent experiments of Ye et al. [J. Ye, D. W. Vernooy, and H. J. Kimble, Phys. Rev. Lett. 83, 4987 (1999)], and find good agreement with the experimental measurements of the distribution of trap occupancy times. The major cause of trap loss arises from the broadening of the energy distribution of the trapped atom, rather than the mean heating rate, which is a very much smaller effect

Active Discriminative Text Representation Learning

We propose a new active learning (AL) method for text classification with convolutional neural networks (CNNs). In AL, one selects the instances to be manually labeled with the aim of maximizing model performance with minimal effort. Neural models capitalize on word embeddings as representations (features), tuning these to the task at hand. We argue that AL strategies for multi-layered neural models should focus on selecting instances that most affect the embedding space (i.e., induce discriminative word representations). This is in contrast to traditional AL approaches (e.g., entropy-based uncertainty sampling), which specify higher level objectives. We propose a simple approach for sentence classification that selects instances containing words whose embeddings are likely to be updated with the greatest magnitude, thereby rapidly learning discriminative, task-specific embeddings. We extend this approach to document classification by jointly considering: (1) the expected changes to the constituent word representations; and (2) the model's current overall uncertainty regarding the instance. The relative emphasis placed on these criteria is governed by a stochastic process that favors selecting instances likely to improve representations at the outset of learning, and then shifts toward general uncertainty sampling as AL progresses. Empirical results show that our method outperforms baseline AL approaches on both sentence and document classification tasks. We also show that, as expected, the method quickly learns discriminative word embeddings. To the best of our knowledge, this is the first work on AL addressing neural models for text classification.Comment: This paper got accepted by AAAI 201

OH(A-X) fluorescence from photodissociative excitation of HO2 at 157.5 nm

The OH(A-X) fluorescence from photodissociative excitation of HO2 by F2 laser photons (157.5 nm) was observed and compared with the OH fluorescence spectra of H2O2 and the O2+CH3OH mixture. The rotational population distributions of OH(A) were obtained from the fluorescence spectra. The most populated levels are J = 4 for photodissociative excitation of HO2, J = 20 for H2O2, and J = 21 for the O2+CH3OH mixture. The fluorescence from the gas mixture is attributed to the O + H recombination for which the atoms are produced from photodissociation of parent molecules