Quantum walk on distinguishable non-interacting many-particles and indistinguishable two-particle

We present an investigation of many-particle quantum walks in systems of non-interacting distinguishable particles. Along with a redistribution of the many-particle density profile we show that the collective evolution of the many-particle system resembles the single-particle quantum walk evolution when the number of steps is greater than the number of particles in the system. For non-uniform initial states we show that the quantum walks can be effectively used to separate the basis states of the particle in position space and grouping like state together. We also discuss a two-particle quantum walk on a two- dimensional lattice and demonstrate an evolution leading to the localization of both particles at the center of the lattice. Finally we discuss the outcome of a quantum walk of two indistinguishable particles interacting at some point during the evolution.Comment: 8 pages, 7 figures, To appear in special issue: "quantum walks" to be published in Quantum Information Processin

Quantum walks: a comprehensive review

Quantum walks, the quantum mechanical counterpart of classical random walks, is an advanced tool for building quantum algorithms that has been recently shown to constitute a universal model of quantum computation. Quantum walks is now a solid field of research of quantum computation full of exciting open problems for physicists, computer scientists, mathematicians and engineers. In this paper we review theoretical advances on the foundations of both discrete- and continuous-time quantum walks, together with the role that randomness plays in quantum walks, the connections between the mathematical models of coined discrete quantum walks and continuous quantum walks, the quantumness of quantum walks, a summary of papers published on discrete quantum walks and entanglement as well as a succinct review of experimental proposals and realizations of discrete-time quantum walks. Furthermore, we have reviewed several algorithms based on both discrete- and continuous-time quantum walks as well as a most important result: the computational universality of both continuous- and discrete- time quantum walks.Comment: Paper accepted for publication in Quantum Information Processing Journa

Track E Implementation Science, Health Systems and Economics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138412/1/jia218443.pd

Color energy of a unitary Cayley graph

Let G be a vertex colored graph. The minimum number χ(G) of colors needed for coloring of a graph G is called the chromatic number. Recently, Adiga et al. [1] have introduced the concept of color energy of a graph Ec(G) and computed the color energy of few families of graphs with χ(G) colors. In this paper we derive explicit formulas for the color energies of the unitary Cayley graph Xn, the complement of the colored unitary Cayley graph $\overline{(Xn)c}$ and some gcd-graphs

Cryptococcus gattii serotype-C strains isolated in Bangalore, Karnataka, India

During a retrospective study on cryptococcosis carried out in Bangalore, Karnataka, India, four Cryptococcus gattii strains were isolated from one HIV-positive and three HIV-negative patients, two of which had unknown predisposing conditions. Serotyping and genotyping showed that the isolates were C. gattii serotype C, mating-type \u3b1 and genotype VGIV. All the isolates were identical by multilocus sequence typing, but presented a low similarity compared with a set of 17 C. gattii global control strains. The comparison with a larger number of previously reported C. gattii strains, including African isolates, revealed a close relationship between Indian and African serotype-C isolates

Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy

Dynamic atomic force microscopy (AFM) is a key platform that enables topological and nanomechanical characterization of novel materials. This is achieved by linking the nanoscale forces that exist between the AFM tip and the sample to specific mathematical functions through modeling. However, the main challenge in dynamic AFM is to quantify these nanoscale forces without the use of complex models that are routinely used to explain the physics of tip–sample interaction. Here, we make use of machine learning and data science to characterize tip–sample forces purely from experimental data with sub-microsecond resolution. Our machine learning approach is first trained on standard AFM models and then showcased experimentally on a polymer blend of polystyrene (PS) and low density polyethylene (LDPE) sample. Using this algorithm we probe the complex physics of tip–sample contact in polymers, estimate elasticity, and provide insight into energy dissipation during contact. Our study opens a new route in dynamic AFM characterization where machine learning can be combined with experimental methodologies to probe transient processes involved in phase transformation as well as complex chemical and biological phenomena in real-time.Dynamics of Micro and Nano SystemsTeam Miguel BessaMicro and Nano Engineerin

Characterizing CO2 Reduction Catalysts on Gas Diffusion Electrodes: Comparing Activity, Selectivity, and Stability of Transition Metal Catalysts

Continued advancements in the electrochemical reduction of CO 2 (CO 2RR) have emphasized that reactivity,selectivity, and stability are not explicit material properties butcombined effects of the catalyst, double-layer, reaction environ-ment, and system configuration. These realizations have steadily built upon the foundational work performed for a broad array of transition metals performed at 5 mA cm−2, which historically guided the research field. To encompass the changing advancements and mindset within the research field, an updated baseline at elevated current densities could then be of value. Here we seek tore-characterize the activity, selectivity, and stability of the five most utilized transition metal catalysts for CO2 RR (Ag, Au, Pd, Sn, and Cu) at elevated reaction rates through electrochemical operation, physical characterization, and varied operating parameters to provide a renewed resource and point of comparison. As a basis, we have employed a common cell architecture, highly controlled catalyst layer morphologies and thicknesses, and fixed current densities. Through a dataset of 88 separate experiments, we provide comparisons between CO-producing catalysts (Ag, Au, and Pd), highlighting CO-limiting current densities on Au and Pd at 72 and 50 mA cm−2, respectively. We further show the instability of Sn in highly alkaline environments, and the convergence of product selectivity at elevated current densities for a Cu catalyst in neutral andalkaline media. Lastly, we reflect upon the use and limits of reaction rates as a baseline metric by comparing catalytic selectivity at 10versus 200 mA cm−2. We hope the collective work provides a resource for researchers setting up CO 2RR experiments for the first time.ChemE/Materials for Energy Conversion & Storag