Transport and magnetic behavior under pressure and high-resolution photoemission studies of Ce2Rh(o.7)Co(0.3)Si3, an alloy on the verge of quantum critical point

We report the influence of external pressure on the temperature dependence of magnetization and electrical resistivity as well as high-resolution photoemission studies for an alloy, Ce2Rh(0.7)Co(0.3)Si3, ordering magnetically below 3 K. It is found that the external pressure has the same effect as that induced by (further) Co substitution for Rh in the series, Ce2Rh(1-x)Co(x)Si3, resulting in qualitative changes in the features in the magnetic and transport data, with a suppression of magnetic ordering followed by quantum critical point effect. The high-resolution spectra reveal signature of Kondo feature at ambient feature. These findings support the validity of spin-density-wave picture in this series.Comment: SCES2010 conferenc

Evolution of the Kondo resonance feature and its relationship to spin-orbit coupling across the quantum critical point in Ce2Rh{1-x}CoxSi3

We investigate the evolution of the electronic structure of Ce2Rh{1-x}CoxSi3 as a function of x employing high resolution photoemission spectroscopy. Co substitution at the Rh sites in antiferromagnetic Ce2RhSi3 leads to a transition from an antiferromagnetic system to a Kondo system, Ce2CoSi3 via the Quantum Critical Point (QCP). High resolution photoemission spectra reveal distinct signature of the Kondo resonance feature (KRF) and its spin orbit split component (SOC) in the whole composition range indicating finite Kondo temperature scale at the quantum critical point. We observe that the intensity ratio of the Kondo resonance feature and its spin orbit split component, KRF/SOC gradually increases with the decrease in temperature in the strong hybridization limit. The scenario gets reversed if the Kondo temperature becomes lower than the magnetic ordering temperature. While finite Kondo temperature within the magnetically ordered phase indicates applicability of the spin density wave picture at the approach to QCP, the dominant temperature dependence of the spin-orbit coupled feature suggests importance of spin-orbit interactions in this regime.Comment: 6 figure

Quickest Paths in Simulations of Pedestrians

This contribution proposes a method to make agents in a microscopic simulation of pedestrian traffic walk approximately along a path of estimated minimal remaining travel time to their destination. Usually models of pedestrian dynamics are (implicitly) built on the assumption that pedestrians walk along the shortest path. Model elements formulated to make pedestrians locally avoid collisions and intrusion into personal space do not produce motion on quickest paths. Therefore a special model element is needed, if one wants to model and simulate pedestrians for whom travel time matters most (e.g. travelers in a station hall who are late for a train). Here such a model element is proposed, discussed and used within the Social Force Model.Comment: revised version submitte

Towards quantum machine learning with tensor networks

Machine learning is a promising application of quantum computing, but challenges remain for implementation today because near-term devices have a limited number of physical qubits and high error rates. Motivated by the usefulness of tensor networks for machine learning in the classical context, we propose quantum computing approaches to both discriminative and generative learning, with circuits based on tree and matrix product state tensor networks, that could already have benefits with such near-term devices. The result is a unified framework in which classical and quantum computing can benefit from the same theoretical and algorithmic developments, and the same model can be trained classically then transferred to the quantum setting for additional optimization. Tensor network circuits can also provide qubit-efficient schemes in which, depending on the architecture, the number of physical qubits required scales only logarithmically with, or independently of the input or output data sizes. We demonstrate our proposals with numerical experiments, training a discriminative model to perform handwriting recognition using a hybrid quantum-classical optimization procedure that could be carried out on quantum hardware today, and testing the noise resilience of the trained model

Universal behavior of quantum Green's functions

We consider a general one-particle Hamiltonian H = - \Delta_r + u(r) defined in a d-dimensional domain. The object of interest is the time-independent Green function G_z(r,r') = . Recently, in one dimension (1D), the Green's function problem was solved explicitly in inverse form, with diagonal elements of Green's function as prescribed variables. The first aim of this paper is to extract from the 1D inverse solution such information about Green's function which cannot be deduced directly from its definition. Among others, this information involves universal, i.e. u(r)-independent, behavior of Green's function close to the domain boundary. The second aim is to extend the inverse formalism to higher dimensions, especially to 3D, and to derive the universal form of Green's function for various shapes of the confining domain boundary.Comment: 46 pages, the shortened version submitted to J. Math. Phy